{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "import requests\n",
    "import os\n",
    "import shutil\n",
    "from datetime import datetime\n",
    "from siyuan_md import *\n",
    "from manager_md import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "note_dir = \"C:\\\\Users\\\\isidore\\\\Nutstore\\\\1\\\\noteasy\"\n",
    "file_df = get_file_df(note_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# rename_by_type(file_df, \"#dataset\")\n",
    "page_remove_empty(file_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(615,\n",
       " 371,\n",
       " 277,\n",
       " ['3d face modeling via weakly-supervised disentanglement network joint identity-consistency prior',\n",
       "  'a closer look at memorization in deep networks',\n",
       "  'a convnet for the 2020s',\n",
       "  'a non-local algorithm for image denoising',\n",
       "  'a theoretical analysis of contrastive unsupervised representation learning',\n",
       "  'accessorize to a crime real and stealthy attacks on state-of-the-art face recognition',\n",
       "  'adaptive l2 regularization in person re-identification',\n",
       "  'adding conditional control to text-to-image diffusion models',\n",
       "  'adversarial autoencoders',\n",
       "  'adversarial examples improve image recognition',\n",
       "  'adversarial examples in the physical world',\n",
       "  'adversarial graph augmentation to improve graph contrastive learning',\n",
       "  'aggregated residual transformations for deep neural networks',\n",
       "  'alignmix improving representation by interpolating aligned features',\n",
       "  'an empirical analysis of the shift and scale parameters in batchnorm',\n",
       "  'an empirical study of training self-supervised vision transformers',\n",
       "  'an image is worth 16x16 words transformers for image recognition at scale',\n",
       "  'analysis of focus measure operators for shape-from-focus',\n",
       "  'approximating cnns with bag-of-local-features models works surprisingly well on imagenet',\n",
       "  'are all negatives created equal in contrastive instance discrimination',\n",
       "  'are graph augmentations necessary simple graph contrastive learning for recommendation',\n",
       "  'attention-aware deep reinforcement learning for video face recognition',\n",
       "  'autoaugment learning augmentation policies from data',\n",
       "  'barlow twins self-supervised learning via redundancy reduction',\n",
       "  'batch normalization explained',\n",
       "  'batch-instance normalization for adaptively style-invariant neural networks',\n",
       "  'batchformer learning to explore sample relationships for robust representation learning',\n",
       "  'batchformerv2 exploring sample relationships for dense representation learning',\n",
       "  'bert pre-training of deep bidirectional transformers for language understanding',\n",
       "  'billion-scale similarity search with gpus',\n",
       "  'bootstrap latent-predictive representations for multitask reinforcement learning',\n",
       "  'bootstrap your own latent a new approach to self-supervised learning',\n",
       "  'bottleneck transformers for visual recognition',\n",
       "  'breaking the softmax bottleneck a high-rank rnn language model',\n",
       "  'byol works even without batch statistics',\n",
       "  'cama class activation mapping disruptive attack for deep neural networks',\n",
       "  'camstyle a novel data augmentation method for person re-identification',\n",
       "  'cascaded diffusion models for high fidelity image generation',\n",
       "  'centernet keypoint triplets for object detection',\n",
       "  'class-aware contrastive semi-supervised learning',\n",
       "  'clinically applicable deep learning for diagnosis and referral in retinal disease',\n",
       "  'cmt convolutional neural networks meet vision transformers',\n",
       "  'co-mining deep face recognition with noisy labels',\n",
       "  'coatnet marrying convolution and attention for all data sizes',\n",
       "  'concept generalization in visual representation learning',\n",
       "  'conformer local features coupling global representations for visual recognition',\n",
       "  'contextual transformer networks for visual recognition',\n",
       "  'contrastive multiview coding',\n",
       "  'controllable and guided face synthesis for unconstrained face recognition',\n",
       "  'convit improving vision transformers with soft convolutional inductive biases',\n",
       "  'costa covariance-preserving feature augmentation for graph contrastive learning',\n",
       "  'cqa-face contrastive quality-aware attentions for face recognition',\n",
       "  'cross-pose lfw  a database for studying cross-pose face recognition in unconstrained environments',\n",
       "  'cross-resolution learning for face recognition',\n",
       "  'crossclr cross-modal contrastive learning for multi-modal video representations',\n",
       "  'cvt introducing convolutions to vision transformers',\n",
       "  'data augmentation for face recognition',\n",
       "  'data augmentation-based joint learning for heterogeneous face recognition',\n",
       "  'deblurring face images with exemplars',\n",
       "  'decoupled networks',\n",
       "  'deep clustering for unsupervised learning of visual features',\n",
       "  'deep features class activation map for thermal face detection and tracking',\n",
       "  'deep learning face representation from predicting 10,000 classes',\n",
       "  'deep parametric continuous convolutional neural networks',\n",
       "  'deep semantic face deblurring',\n",
       "  'deephash getting regularization, depth and finetuning right',\n",
       "  'deeply learned face representations are sparse, selective, and robust',\n",
       "  'demystifying local vision transformer sparse connectivity, weight sharing, and dynamic weight',\n",
       "  'dependency-aware attention control for unconstrained face recognition with image sets',\n",
       "  'designing network design spaces',\n",
       "  'detecting camouflaged object in frequency domain',\n",
       "  'discriminative learning of local image descriptors',\n",
       "  'discriminative learning quadratic discriminant function for handwriting recognition',\n",
       "  'discriminative unsupervised feature learning with exemplar convolutional neural networks',\n",
       "  'disentangled representation learning gan for pose-invariant face recognition',\n",
       "  'do better imagenet models transfer better',\n",
       "  'do deep nets really need weight decay and dropout',\n",
       "  'do normalization layers in a deep convnet really need to be distinct',\n",
       "  'do vision transformers see like convolutional neural networks',\n",
       "  'dreambooth fine tuning text-to-image diffusion models for subject-driven generation',\n",
       "  'dvg-face dual variational generation for heterogeneous face recognition',\n",
       "  'early convolutions help transformers see better',\n",
       "  'efficient backprop',\n",
       "  'emerging properties in self-supervised vision transformers',\n",
       "  'eqface a simple explicit quality network for face recognition',\n",
       "  'evaluation of prototype learning algorithms for nearest-neighbor classifier in application to handwritten character recognition',\n",
       "  'explaining and harnessing adversarial examples',\n",
       "  'exploring plain vision transformer backbones for object detection',\n",
       "  'exploring racial bias within face recognition via per-subject adversarially-enabled data augmentation',\n",
       "  'exploring simple siamese representation learning',\n",
       "  'exploring the equivalence of siamese self-supervised learning via a unified gradient framework',\n",
       "  'f-gan training generative neural samplers using variational divergence minimization',\n",
       "  'face recognition in low quality images a survey',\n",
       "  'face-specific data augmentation for unconstrained face recognition',\n",
       "  'feature aggregation network for video face recognition',\n",
       "  'few-shot learning with graph neural networks',\n",
       "  'fixing the train-test resolution discrepancy',\n",
       "  'focusface multi-task contrastive learning for masked face recognition',\n",
       "  'formal limitations on the measurement of mutual information',\n",
       "  'four things everyone should know to improve batch normalization',\n",
       "  'frame-wise action representations for long videos via sequence contrastive learning',\n",
       "  'generalizing across domains via cross-gradient training',\n",
       "  'generate to adapt resolution adaption network for surveillance face recognition',\n",
       "  'generative adversarial nets',\n",
       "  'geometric representation of high dimension, low sample size data',\n",
       "  \"grace generating concise and informative contrastive sample to explain neural network model's prediction\",\n",
       "  'graph contrastive learning automated',\n",
       "  'graph contrastive learning with augmentations',\n",
       "  'group normalization',\n",
       "  'highly scalable deep learning training system with mixed-precision training imagenet in four minutes',\n",
       "  'how contextual are contextualized word representations comparing the geometry of bert, elmo, and gpt-2 embeddings',\n",
       "  'how does batch normalization help optimization',\n",
       "  'how to fine-tune bert for text classification',\n",
       "  'hubness and pollution delving into cross-space mapping for zero-shot learning',\n",
       "  'hybridcr weakly-supervised 3d point cloud semantic segmentation via hybrid contrastive regularization',\n",
       "  'i-secret importance-guided fundus image enhancement via semi-supervised contrastive constraining',\n",
       "  'icarl incremental classifier and representation learning',\n",
       "  'image retrieval by shape a comparative study',\n",
       "  'imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness',\n",
       "  'implicit semantic data augmentation for deep networks',\n",
       "  'improved baselines with momentum contrastive learning',\n",
       "  'improved deep metric learning with multi-class n-pair loss objective',\n",
       "  'improving face recognition from hard samples via distribution distillation loss',\n",
       "  'in learning to learn',\n",
       "  'incorporating convolution designs into visual transformers',\n",
       "  'inflated episodic memory with region self-attention for long-tailed visual recognition',\n",
       "  'intriguing properties of contrastive losses',\n",
       "  'joint embeddings of shapes and images via cnn image purification',\n",
       "  'l2 regularization versus batch and weight normalization',\n",
       "  'large-scale long-tailed recognition in an open world',\n",
       "  'learning a unified classifier incrementally via rebalancing',\n",
       "  'learning deep features for discriminative localization',\n",
       "  'learning deep representations by mutual information estimation and maximization',\n",
       "  'learning discriminative aggregation network for video-based face recognition',\n",
       "  'learning local feature descriptors using convex optimisation',\n",
       "  'learning local feature descriptors with triplets and shallow convolutional neural networks',\n",
       "  'learning local image descriptors with deep siamese and triplet convolutional networks by minimising global loss functions',\n",
       "  'learning multiple adverse weather removal via two-stage knowledge learning and multi-contrastive regularization toward a unified model',\n",
       "  'learning robust global representations by penalizing local predictive power',\n",
       "  'learning spread-out local feature descriptors',\n",
       "  'learning to learn by gradient descent by gradient descent',\n",
       "  'learning to resize images for computer vision tasks',\n",
       "  'local relation networks for image recognition',\n",
       "  'locality guidance for improving vision transformers on tiny datasets',\n",
       "  'low-resolution face alignment and recognition using mixed-resolution classifiers',\n",
       "  'mean teachers are better role models weight-averaged consistency targets improve semi-supervised deep learning results',\n",
       "  'memory-augmented relation network for few-shot learning',\n",
       "  'memorybased neighbourhood embedding for visual recognition',\n",
       "  'mentornet learning data-driven curriculum for very deep neural networks on corrupted labels',\n",
       "  'mind-net a deep mutual information distillation network for realistic low-resolution face recognition',\n",
       "  'mine mutual information neural estimation',\n",
       "  'mixup beyond empirical risk minimization',\n",
       "  'mobile-former bridging mobilenet and transformer',\n",
       "  'mobilenets efficient convolutional neural networks for mobile vision applications',\n",
       "  'mobilenetv2 inverted residuals and linear bottlenecks',\n",
       "  'modelling uncertainty in representation of facial features for face recognition',\n",
       "  'multicolumn networks for face recognition',\n",
       "  'multidimensional scaling for matching low-resolution face images',\n",
       "  'multiscale vision transformers',\n",
       "  'mvitv2 improved multiscale vision transformers for classification and detection',\n",
       "  'naive-deep face recognition touching the limit of lfw benchmark or not',\n",
       "  'neural aggregation network for video face recognition',\n",
       "  'next-vit next generation vision transformer for efficient deployment in realistic industrial scenarios',\n",
       "  'noise-tolerant paradigm for training face recognition cnns',\n",
       "  'non-local neural networks',\n",
       "  'npt-loss demystifying face recognition losses with nearest proxies triplet',\n",
       "  'objects as points',\n",
       "  'on feature normalization and data augmentation',\n",
       "  'on largebatch training for deep learning generalization gap and sharp minima',\n",
       "  'on low-resolution face recognition in the wild comparisons and new techniques',\n",
       "  'on mutual information maximization for representation learning',\n",
       "  'on the integration of self-attention and convolution',\n",
       "  'on the periodic behavior of neural network training with batch normalization and weight decay',\n",
       "  'on the relationship between selfattention and convolutional layers',\n",
       "  'on variational bounds of mutual information',\n",
       "  'online adaptation for consistent mesh reconstruction in the wild',\n",
       "  'parc-net position aware circular convolution with merits from convnets and transformer',\n",
       "  'parn position-aware relation networks for few-shot learning',\n",
       "  'perceptual straightening of natural videos',\n",
       "  'person re-identification in the wild',\n",
       "  'perturbed self-distillation weakly supervised large-scale point cloud semantic segmentation',\n",
       "  'pointaugment an auto-augmentation framework for point cloud classification',\n",
       "  'popular nearest neighbors in high-dimensional data',\n",
       "  'probabilistic elastic matching for pose variant face verification',\n",
       "  'probabilistic face embeddings',\n",
       "  'prototypical contrastive learning of unsupervised representations',\n",
       "  'qmagface simple and accurate quality-aware face recognition',\n",
       "  'quality aware network for set to set recognition',\n",
       "  'recurrent embedding aggregation network for video face recognition',\n",
       "  'reducing duplicate filters in deep neural networks',\n",
       "  'region-based quality estimation network for large-scale person re-identification',\n",
       "  'regularizing cnns with locally constrained decorrelations',\n",
       "  'representation learning with contrastive predictive coding',\n",
       "  'resolution invariant face recognition using a distillation approach',\n",
       "  'rethinking batch in batchnorm',\n",
       "  'rethinking depthwise separable convolutions how intra-kernel correlations lead to improved mobilenets',\n",
       "  'revisiting unreasonable effectiveness of data in deep learning era',\n",
       "  'ridge regression, hubness, and zero-shot learning',\n",
       "  'scalable person reidentification a benchmark',\n",
       "  'scaling sgd batch size to 32k for imagenet training',\n",
       "  'searching for activation functions',\n",
       "  'searching for mobilenetv3',\n",
       "  'self-guided learning to denoise for robust recommendation',\n",
       "  'self-supervised graph learning for recommendation',\n",
       "  'self-supervised graph-level representation learning with local and global structure',\n",
       "  'self-supervised single-view 3d reconstrusction via semantic consistency',\n",
       "  'selfattention with relative position representations',\n",
       "  'semantically contrastive learning for low-light image enhancement',\n",
       "  'semi-supervised semantic image segmentation with self-correcting networks',\n",
       "  'separability-oriented subclass discriminant analysis',\n",
       "  'short-conformer convolution-augmented transformer for speech recognition',\n",
       "  'significance of softmax-based features over metric learning-based features',\n",
       "  'simmim a simple framework for masked image modeling',\n",
       "  'simultaneous hallucination and recognition of low-resolution faces based on singular value decomposition',\n",
       "  'sparse low-rank component-based representation for face recognition with low-quality images',\n",
       "  'spherical confidence learning for face recognition',\n",
       "  'squeeze-and-excitation networks',\n",
       "  'stand-alone selfattention in vision models',\n",
       "  'subcenter arcface boosting face recognition by large-scale noisy web faces',\n",
       "  'subspace clustering',\n",
       "  'super-resolving very low-resolution face images with supplementary attributes',\n",
       "  'swin transformer hierarchical vision transformer using shifted windows',\n",
       "  'swin transformer v2 scaling up capacity and resolution',\n",
       "  'tcct tightly-coupled convolutional transformer on time series forecasting',\n",
       "  'tcn transferable coupled network for cross-resolution face recognition',\n",
       "  'texture synthesis and the controlled generation of natural stimuli using convolutional neural networks',\n",
       "  'the feret database and evaluation procedure for face-recognition algorithms',\n",
       "  'the origins and prevalence of texture bias in convolutional neural networks',\n",
       "  'the predictive mind',\n",
       "  'the unreasonable effectiveness of deep features as a perceptual metric',\n",
       "  'theoretically principled trade-off between robustness and accuracy',\n",
       "  'three mechanisms of weight decay regularization',\n",
       "  'tighter variational bounds are not necessarily better',\n",
       "  'towards learning spatially discriminative feature representations',\n",
       "  'towards open-world segmentation of parts',\n",
       "  'towards understanding cross resolution feature matching for surveillance face recognition',\n",
       "  'towards universal representation learning for deep face recognition',\n",
       "  'tracking emerges by colorizing videos',\n",
       "  'train longer, generalize better closing the generalization gap in large batch training of neural networks',\n",
       "  'training data-efficient image transformers & distillation through attention',\n",
       "  'training deep nets with sublinear memory cost',\n",
       "  'transferability in machine learning from phenomena to black-box attacks using adversarial samples',\n",
       "  'trunk-branch ensemble convolutional neural networks for video-based face recognition',\n",
       "  'tryondiffusion a tale of two unets',\n",
       "  'understanding and improving convolutional neural networks via concatenated rectified linear units',\n",
       "  'understanding batch normalization',\n",
       "  'understanding deep learning requires rethinking generalization',\n",
       "  'understanding neural networks through deep visualization',\n",
       "  'understanding the difficulty of training deep feedforward neural networks',\n",
       "  'unequal-training for deep face recognition with long-tailed noisy data',\n",
       "  'unpaired caricature-visual face recognition via feature decomposition-restoration-decomposition',\n",
       "  'unsupervised embedding learning via invariant and spreading instance feature',\n",
       "  'unsupervised learning of visual features by contrasting cluster assignments',\n",
       "  'unsupervised visual representation learning by context prediction',\n",
       "  'valuation of output embeddings for fine-grained image classification',\n",
       "  'vico detail-preserving visual condition for personalized text-to-image generation',\n",
       "  'video face recognition component-wise feature aggregation network (c-fan)',\n",
       "  'view-all',\n",
       "  'view-archived',\n",
       "  'view-detailed',\n",
       "  'view-doing',\n",
       "  'view-empty',\n",
       "  'view-finished',\n",
       "  'view-holding',\n",
       "  'view-normal',\n",
       "  'visformer the vision-friendly transformer',\n",
       "  'weakly supervised video moment localization with contrastive negative sample mining',\n",
       "  'weight normalization a simple reparameterization to accelerate training of deep neural networks',\n",
       "  'what are people asking about covid-19',\n",
       "  'when does contrastive visual representation learning work',\n",
       "  'when does label smoothing help',\n",
       "  'why do deep convolutional networks generalize so poorly to small image transformations',\n",
       "  'why l2 normalized loss with margin works',\n",
       "  'writing',\n",
       "  'wsabie scaling up to large vocabulary image annotation',\n",
       "  'xsimgcl towards extremely simple graph contrastive learning for recommendation',\n",
       "  'zero memory optimizations toward training trillion parameter models'])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m在当前单元格或上一个单元格中执行代码时 Kernel 崩溃。请查看单元格中的代码，以确定故障的可能原因。有关详细信息，请单击 <a href='https://aka.ms/vscodeJupyterKernelCrash'>此处</a>。有关更多详细信息，请查看 Jupyter <a href='command:jupyter.viewOutput'>log</a>。"
     ]
    }
   ],
   "source": [
    "vika_papers = \"\"\"Prototypical Networks for Few-shot Learning\n",
    "Learning deep representations of fine-grained visual descriptions\n",
    "Matching networks for one shot learning\n",
    "Optimization as a model for few-shot learning\n",
    "Clustering with bregman divergences\n",
    "Knowledge Evolution in Neural Networks\n",
    "TileGAN Synthesis of Large Scale Non Homogeneous Textures\n",
    "SOFT: Softmax-free Transformer with Linear Complexity\n",
    "Top 10 algorithms in data mining\n",
    "Involution Inverting the Inherence of Convolution for Visual Recognition\n",
    "Averaging weights leads to wider optima and better generalization\n",
    "Towards a neural statistician\n",
    "A Two-phase Prototypical Network Model for Incremental Few-shot Relation Classification\n",
    "Few-Shot Text Classification with Triplet Networks, Data Augmentation, and Curriculum Learning\n",
    "Few-shot Pseudo-Labeling for Intent Detection\n",
    "AugNLG: Few-shot Natural Language Generation using Self-trained Data Augmentation\n",
    "Training highly multiclass classifiers\n",
    "Curriculum learning\n",
    "Rethinking Curriculum Learning With Incremental Labels And Adaptive Compensation\n",
    "News Category Dataset\n",
    "FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation\n",
    "What Are People Asking About COVID-19?\n",
    "Hierarchical text classification of Amazon product reviews\n",
    "EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks\n",
    "Improving Answer Selection and Answer Triggering using Hard Negatives\n",
    "Deep Metric Learning via Lifted Structured Feature Embedding\n",
    "Few-shot natural language generation for task-oriented dialog\n",
    "Schemaguided dialogue state tracking task at dstc8\n",
    "Multiwoz-a large-scale multi-domain wizard-of-oz dataset for task-oriented dialogue modelling\n",
    "A repository of conversational datasets\n",
    "Hierarchical grouping to optimize an objective function\n",
    "Deep metric transfer for label propagation with limited annotated data\n",
    "Snips voice platform: an embedded spoken language understanding system for private-by-design voice interfaces\n",
    "An evaluation dataset for intent classification and out-of-scope prediction\n",
    "Benchmarking natural language understanding services for building conversational agents\n",
    "Topic detection model in a single-domain corpus inspired by the human memory cognitive process\n",
    "How to fine-tune bert for text classification?\n",
    "Huggingface’s transformers: State-of-the-art natural language processing\n",
    "Meta-learning for semi-supervised few-shot classification\n",
    "Gaussian prototypical networks for few-shot learning on omniglot\n",
    "Robust classification with convolutional prototype learning\n",
    "Hybrid attention-based prototypical networks for noisy few-shot relation classification\n",
    "Large margin prototypical network for few-shot relation classification with fine-grained features\n",
    "Dynamic few-shot visual learning without forgetting\n",
    "Revisiting Self-Training for Few-Shot Learning of Language Model\n",
    "Language models are few-shot learners\n",
    "Fixmatch: Simplifying semi-supervised learning with consistency and confidence\n",
    "Making pre-trained language models better few-shot learners\n",
    "Simcse: Simple contrastive learning of sentence embbeddings\n",
    "Recursive deep models for semantic compositionality over a sentiment tree-bank\n",
    "Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales\n",
    "Mining and summarizing customer reviews\n",
    "Annotating expressions of opinions and emotions in language\n",
    "A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts\n",
    "A broad-coverage challenge corpus for sentence understanding through inference\n",
    "A large annotated corpus for learning natural language inference\n",
    "SQuAD: 100,000+ questions for machine comprehension of text\n",
    "The pascal recognising textual entailment challenge\n",
    "Automatically constructing a corpus of sentential paraphrases\n",
    "Exploiting cloze-questions for few-shot text classification and natural language inference\n",
    "Theoretical analysis of self-training with deep networks on unlabeled data\n",
    "Multi-Level Matching and Aggregation Network for Few-Shot Relation Classification\n",
    "Order matters: Sequence to sequence for sets\n",
    "Neighbourhood component analysis\n",
    "Siamese neural networks for one-shot image recognition\n",
    "Building a large annotated corpus of english: The penn treebank\n",
    "Masked Autoencoders Are Scalable Vision Learners\n",
    "Visualizing data using t-SNE\n",
    "Learning to Compare: Relation Network for Few-Shot Learning\n",
    "Zero-shot learning-a comprehensive evaluation of the good, the bad and the ugly\n",
    "Generalizing from a Few Examples: A Survey on Few-shot Learning\n",
    "Learning a deep embedding model for zero-shot learning\n",
    "Human-level concept learning through probabilistic program induction\n",
    "Few-shot and zero-shot multi-label learning for structured label spaces\n",
    "Few-Shot Emotion Recognition in Conversation with Sequential Prototypical Networks\n",
    "Disentangling 3D Prototypical Networks for Few-Shot Concept Learning\n",
    "Ensemble Making Few-Shot Learning Stronger\n",
    "GIRAFFE: representation Scenes as composition Generative Neural Feature Fields\n",
    "GRAF: Generative Radiance Fields for 3D-Aware Image Synthesis\n",
    "Learning spatial common sense with geometry-aware recurrent networks\n",
    "Learning from unlabelled videos with contrastive predictive neural 3d mapping\n",
    "End-to-End Object Detection with Transformers\n",
    "Deformable DETR: Deformable Transformers for End-to-End Object Detection\n",
    "SinGAN: Learning a Generative Model from a Single Natural Image\n",
    "Learning Continuous Image Representation with Local Implicit Image Function\n",
    "Arbitrary style transfer in real-time with adaptive instance normalization\n",
    "CLEVR: A diagnostic dataset for compositional language and elementary visual reasoning\n",
    "The Replica dataset: A digital replica of indoor spaces\n",
    "Habitat: A Platform for Embodied AI Research\n",
    "The Neuro Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision\n",
    "Understanding contrastive representation learning through alignment and uniformity on the hypersphere\n",
    "On sampling strategies for neural networkbased collaborative filtering\n",
    "A simple framework for contrastive learning of visual representations\n",
    "SemEval-2017 Task 1: Semantic Textual Similarity Multilingual and Crosslingual Focused Evaluation\n",
    "SentenceBERT: Sentence embeddings using Siamese BERTnetworks\n",
    "From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions\n",
    "ParaNMT50M: Pushing the limits of paraphrastic sentence embeddings with millions of machine translations\n",
    "How contextual are contextualized word representations? comparing the geometry of BERT, ELMo, and GPT-2 embeddings\n",
    "On the trace and the sum of elements of a matrix\n",
    "A SICK cure for the evaluation of compositional distributional semantic models\n",
    "SemEval-2015 task 2: Semantic textual similarity, English, Spanish and pilot on interpretability\n",
    "SemEval-2014 task 10: Multilingual semantic textual similarity\n",
    "SemEval-2016 task 1: Semantic textual similarity, monolingual and cross-lingual evaluation\n",
    "SemEval-2012 task 6: A pilot on semantic textual similarity\n",
    "SEM 2013 shared task: Semantic Textual Similarity\n",
    "Task-Oriented Intrinsic Evaluation of Semantic Textual Similarity\n",
    "Building a Question Answering Test Collection\n",
    "MLP-Mixer: An all-MLP Architecture for Vision\n",
    "One shot learning of simple visual concepts\n",
    "A fast learning algorithm for deep belief nets\n",
    "Pattern Analysis and Machine Intelligence\n",
    "Signature verification using a siamese time delay neural network\n",
    "The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks\n",
    "Talk-to-Edit: Fine-Grained Facial Editing via Dialog\n",
    "Decoupled Weight Decay Regularization\n",
    "孪生网络\n",
    "度量学习\n",
    "Learning to Detect Every Thing in an Open World\n",
    "SCOPS: Self-Supervised Co-Part Segmentation\n",
    "Motion-supervised Co-Part Segmentation\n",
    "Self-Supervised Viewpoint Learning From Image Collections\n",
    "Self-supervised Single-View 3D Reconstruction via Semantic Consistency\n",
    "Hologan: Unsupervised learning of 3d representations from natural images\n",
    "Learning category-specific mesh reconstruction from image collections\n",
    "Joint-task Self-supervised Learning for Temporal Correspondence\n",
    "Perceptual Losses for Real-Time Style Transfer and Super-Resolution\n",
    "NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis\n",
    "GRAF: Generative Radiance Fields for 3D-Aware Image Synthesis\n",
    "GIRAFFE: Representing Scenes As Compositional Generative Neural Feature Fields\n",
    "pixelNeRF: Neural Radiance Fields From One or Few Images\n",
    "Learning in High Dimension Always Amounts to Extrapolation\n",
    "Switchable temporal propagation network\n",
    "本征分解\n",
    "Fast Spatio-Temporal Residual Network for Video Super-Resolution\n",
    "Progressive Reconstruction of Visual Structure for Image Inpainting\n",
    "Recurrent Feature Reasoning for Image Inpainting\n",
    "Self-Ensembling Attention Networks: Addressing Domain Shift for Semantic Segmentation\n",
    "R-SVM+: Robust Learning with Privileged Information\n",
    "Robust Learning with Imperfect Priviledged Information\n",
    "Superpixel-Based Intrinsic Image Decomposition of Hyperspectral Images\n",
    "Intrinsic Image Recovery From Remote Sensing Hyperspectral Images\n",
    "Intrinsic Hyperspectral Image Decomposition With DSM Cues\n",
    "Supervoxel-Based Intrinsic Scene Properties From Hyperspectral Images and LiDAR\n",
    "MULTI-TEMPORAL SAR IMAGE DESPECKLING BASED A CONVOLUTIONAL NEURAL NETWORK\n",
    "UAV-based integrated multispectral-LiDAR imaging system and data processing\n",
    "Multimodal hyperspectral remote sensing: an overview and perspective\n",
    "Detection of Event of Interest for Satellite Video Understanding\n",
    "LocalDrop: A Hybrid Regularization for Deep Neural Networks\n",
    "Fully Convolutional Networks for Semantic Segmentation\n",
    "Making Convolutional Networks Shift-Invariant Again\n",
    "Why do deep convolutional networks generalize so poorly to small image transformations?\n",
    "Shiftable multiscale transforms\n",
    "A Simple Framework for Contrastive Learning of Visual Representations\n",
    "Normface: L2 hypersphere embedding for face verification\n",
    "Hyperspherical Prototype Networks\n",
    "Spherical Latent Spaces for Stable Variational Autoencoders\n",
    "Auto-Encoding Variational Bayes\n",
    "Deep Hyperspherical Learning\n",
    "Cosine Normalization: Using Cosine Similarity Instead of Dot Product in Neural Networks\n",
    "Self-organization in a perceptual network\n",
    "Universally optimal distribution of points on spheres\n",
    "Discrete energy on rectifiable sets\n",
    "An analysis of single-layer networks in unsupervised feature learning\n",
    "Indoor segmentation and support inference from rgbd images\n",
    "Aligning books and movies: Towards story-like visual explanations by watching movies and reading books\n",
    "An efficient framework for learning sentence representations\n",
    "Momentum contrast for unsupervised visual representation learning\n",
    "K-BERT: Enabling Language Representation with Knowledge Graph\n",
    "Accurate, large minibatch sgd: Training imagenet in 1 hour\n",
    "Large batch training of convolutional networks\n",
    "Sgdr: Stochastic gradient descent with warm restarts\n",
    "Learning representations by maximizing mutual information across views\n",
    "Data-efficient image recognition with contrastive predictive coding\n",
    "Autoaugment: Learning augmentation strategies from data\n",
    "wolfram\n",
    "Unsupervised feature learning via non-parametric instance discrimination\n",
    "Do better ImageNet models transfer better?\n",
    "Discriminative unsupervised feature learning with convolutional neural networks\n",
    "Deep Face Recognition\n",
    "Facenet: A unified embedding for face recognition and clustering\n",
    "DeepFace: Closing the Gap to Human-Level Performance in Face Verification\n",
    "Fisher vector faces in the wild\n",
    "Face description with local binary patterns: Application to face recognition\n",
    "Labeled faces in the wild: A database for studying face recognition in unconstrained environments\n",
    "Face recognition in unconstrained videos with matched background similarity\n",
    "Improving the Fisher kernel for large-scale image classification\n",
    "Efficient match kernels between sets of features for visual recognition\n",
    "Exploiting generative models in discriminative classifiers\n",
    "Fisher kernels on visual vocabularies for image categorization\n",
    "Introduction to gaussian processes\n",
    "The nature of statistical learning theory\n",
    "Natural gradient works efficiently in learning\n",
    "Visual categorization with bags of keypoints\n",
    "Adapted vocabularies for generic visual categorization\n",
    "Improving “bag-of-keypoints” image categorisation\n",
    "Constructing visual models with a latent space approach\n",
    "Theory of Keyblock-based image retrieval\n",
    "Classifying materials from images: to cluster or not to cluster\n",
    "Unsupervised part based disentangling of object shape and appearance\n",
    "First order motion model for image animation\n",
    "Voxceleb: a large-scale speaker identification dataset\n",
    "Spatial transformer networks\n",
    "Self-Supervised 3D Mesh Reconstruction From Single Images\n",
    "What Is Considered Complete for Visual Recognition\n",
    "Reconciling modern machine learning and the bias-variance trade-off\n",
    "Rich feature hierarchies for accurate object detection and semantic segmentation\n",
    "Fast RCNN\n",
    "Faster r-cnn: Towards real-time object detection with region proposal networks\n",
    "Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition\n",
    "DER: Dynamically Expandable Representation for Class Incremental Learning\n",
    "Big Self-Supervised Models are Strong Semi-Supervised Learners\n",
    "Bag of tricks for image classification with convolutional neural networks\n",
    "Rethinking the inception architecture for computer vision\n",
    "Video google: A text retrieval approach to object matching in videos\n",
    "Categorizing nine visual classes using local appearance descriptors\n",
    "Wide & Deep Learning for Recommender System\n",
    "Attention is All You Need\n",
    "ALBERT: A Lite BERT for Self-supervised Learning of Language Representations\n",
    "A Robustly Optimized BERT Pretraining Approach\n",
    "Selective Kernel Networks\n",
    "Rethinking Feature Discrimination and Polymerization for Large-scale Recognition\n",
    "Sphereface: Deep hypersphere embedding for face recognition\n",
    "Large-margin softmax loss for convolutional neural networks\n",
    "L2-constrained softmax loss for discriminative face verification\n",
    "Learning a metric embedding for face recognition using the multibatch method\n",
    "A discriminative feature learning approach for deep face recognition\n",
    "Deep Convolutional Neural Network Features and the Original Image\n",
    "Riemannian walk for incremental learning: Understanding forgetting and intransigence\n",
    "Conditional channel gated networks for task-aware continual learning\n",
    "Random path selection for continual learning\n",
    "Overcoming catastrophic forgetting with hard attention to the task\n",
    "Heated-up softmax embedding\n",
    "End-to-end incremental learning\n",
    " icarl: Incremental classifier and representation learning\n",
    " Learning a unified classifier incrementally via rebalancing\n",
    "Herding dynamical weights to learn\n",
    "Podnet: Pooled outputs distillation for small-tasks incremental learning\n",
    "Deep nearest class mean classifiers\n",
    "Von Mises-Fisher mixture model-based deep learning: Application to face verification\n",
    "UniformFace: Learning Deep Equidistributed Representation for Face Recognition\n",
    "CosFace: Large Margin Cosine Loss for Deep Face Recognition\n",
    "Cam-softmax for discriminative deep feature learning\n",
    "Toward open set recognition\n",
    "ArcFace: Additive Angular Margin Loss for Deep Face Recognition\n",
    "Learning towards minimum hyperspherical energy\n",
    "IntraLoss: Further Margin via Gradient-Enhancing Term for Deep Face Recognition\n",
    "Additive Margin Softmax for Face Verification\n",
    "TypicFace: Dynamic Margin Cosine Loss for Deep Face Recognition\n",
    "RegularFace: Deep Face Recognition via Exclusive Regularization\n",
    "Fair Loss: Margin-Aware Reinforcement Learning for Deep Face Recognition\n",
    "Class-Variant Margin Normalized Softmax Loss for Deep Face Recognition\n",
    "Qamface: Quadratic Additive Angular Margin Loss For Face Recognition\n",
    "Mis-classified Vector Guided Softmax Loss for Face Recognition\n",
    "Orthogonality Loss: Learning Discriminative Representations for Face Recognition\n",
    "Learning spreadout local feature descriptors\n",
    "Ole: Orthogonal low-rank embedding-a plug and play geometric loss for deep learning\n",
    "Deep hashing for compact binary codes learning\n",
    "Feature transfer learning for deep face recognition with under-represented data\n",
    "One-shot face recognition by promoting underrepresented classes\n",
    "Ring Loss: Convex Feature Normalization for Face Recognition\n",
    "Range loss for deep face recognition with long-tailed training data\n",
    "Noisy softmax: Improving the generalization ability of DCNN via postponing the early softmax saturation\n",
    "Disturblabel: Regularizing cnn on the loss layer\n",
    "Naive-deep face recognition: Touching the limit of lfw benchmark or not?\n",
    "ImageNet Large Scale Visual Recognition Challenge\n",
    "Training region-based object detectors with online hard example mining\n",
    "Focal loss for dense object detection\n",
    "The devil of face recognition is in the noise\n",
    "MS-Celeb-1M: A dataset and benchmark for large-scale face recognition\n",
    "The megaface benchmark: 1 million faces for recognition at scale\n",
    " The feret database and evaluation procedure for face-recognition algorithms\n",
    "Deep learning face representation by joint identification-verification\n",
    "Learning face representation from scratch\n",
    "A data-driven approach to cleaning large face datasets\n",
    "The unreasonable effectiveness of noisy data for fine-grained recognition\n",
    "Deep learning is robust to massive label noise\n",
    "Cross-pose LFW:: A database for studying cross-pose face recognition in unconstrained environments\n",
    "Crossage LFW: A database for studying cross-age face recognition in unconstrained environments\n",
    "Human-level control through deep reinforcement learning\n",
    "Intriguing properties of neural networks\n",
    "Distilling the Knowledge in a Neural Network\n",
    "3D Object Representations for FineGrained Categorization\n",
    "Caltech-UCSD Birds 200\n",
    "Deepfashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations\n",
    "Learning and Example Selection for Object and Pattern Detection\n",
    "Dropout as Data Augmentation\n",
    "Dimensionality reduction by learning an invariant mapping\n",
    "Metric learning with adaptive density discrimination\n",
    "Discovering hidden factors of variation in deep networks\n",
    "Reducing overfitting in deep networks by decorrelating representations\n",
    "Natural neural networks\n",
    "Learning transformations for clustering and classification\n",
    "Feature pyramid networks for object detection\n",
    "In Defense of the Triplet Loss for Person Re-Identification\n",
    "Distance Metric Learning for Large Margin Nearest Neighbor Classification\n",
    "MARS: A Video Benchmark for Large-Scale Person Re-Identification\n",
    "Re-ranking Person Re-identification with k-reciprocal Encoding\n",
    "Beyond Synthetic Noise: Deep Learning on Controlled Noisy Labels\n",
    "ElasticFace: Elastic Margin Loss for Deep Face Recognition\n",
    "Agedb: The first manually collected, in-the-wild age database\n",
    "Frontal to profile face verification in the wild\n",
    "Vggface2: A dataset for recognising faces across pose and age\n",
    "IARPA janus benchmark - C: face dataset and protocol\n",
    "IARPA janus benchmark-b face dataset\n",
    "Labeled Faces in the Wild: Updates and New Reporting Procedures\n",
    "A benchmark study of large-scale unconstrained face recognition\n",
    "Identity Mappings in Deep Residual Networks\n",
    "Training very deep networks\n",
    "Batch normalization: accelerating deep network training by reducing internal covariate shift\n",
    "Deep Pyramidal Residual Networks\n",
    "Residual networks behave like ensembles of relatively shallow networks\n",
    "Training and investigating residual nets\n",
    "Weighted residuals for very deep networks\n",
    "Deep networks with stochastic depth\n",
    "Deep Residual Learning for Image Recognition\n",
    "Deep Residual Learning for Image Recognition\n",
    "Improved Residual Networks for Image and Video Recognition\n",
    "Noisy activation functions\n",
    "Dyn-arcface: dynamic additive angular margin loss for deep face recognition\n",
    "知识表示学习研究进展\n",
    "How to grow a mind\n",
    "Structured statistical models of inductive reasoning\n",
    "K-BERT: Enabling Language Representation with Knowledge Graph\n",
    "Rule-Guided Compositional Representation Learning on Knowledge Graphs\n",
    "Modeling Relation Paths for Representation Learning of Knowledge Bases\n",
    "InteractE: Improving Convolution-Based Knowledge Graph Embeddings by Increasing Feature Interactions\n",
    "Convolutional 2D Knowledge Graph Embeddings\n",
    "RotatE: Knowledge Graph Embedding By Relational Rotation in Complex Space\n",
    "Learning Structured Embeddings of Knowledge Bases\n",
    "Non-local manifold parzen windows\n",
    "Translating Embeddings for Modeling Multi-relational Data\n",
    "Long-tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution Networks\n",
    "Hierarchical relation extraction with coarse-to-fine grained attention.\n",
    "Modeling relations and their mentions without labeled text\n",
    "Knowledgebased weak supervision for information extraction of overlapping relations\n",
    "Knowledge graph embedding with hierarchical relation structure\n",
    "SelfKG: Self-Supervised Entity Alignment in Knowledge Graphs\n",
    "Noise-contrastive estimation: A new estimation principle for unnormalized statistical models\n",
    "Type-augmented Relation Prediction in Knowledge Graphs\n",
    "Quaternion Knowledge Graph Embeddings\n",
    "Complex embeddings for simple link prediction\n",
    "AdaCos: Adaptively Scaling Cosine Logits for Effectively Learning Deep Face Representations\n",
    "Revisiting Local Descriptor based Image-to-Class Measure for Few-shot Learning\n",
    "Circle Loss: A Unified Perspective of Pair Similarity Optimization\n",
    "EASY – Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple Ingredients\n",
    "It’s DONE: Direct ONE-shot learning with Hebbian weight imprinting\n",
    "Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles\n",
    "Low-Shot Learning With Imprinted Weights\n",
    "Exploring the Limits of Out-of-Distribution Detection\n",
    "MagFace: A Universal Representation for Face Recognition and Quality Assessment\n",
    "AdaFace: Quality Adaptive Margin for Face Recognition\n",
    "CurricularFace: Adaptive Curriculum Learning Loss for Deep Face Recognition\n",
    "AdaptiveFace: Adaptive Margin and Sampling for Face Recognition\n",
    "Large-scale Bisample Learning on ID Versus Spot Face Recognition\n",
    "P2SGrad: Refined Gradients for Optimizing Deep Face Models\n",
    "BroadFace: Looking at Tens of Thousands of People at Once for Face Recognition\n",
    "End-to-End Object Detection with Transformers\n",
    "Prototype Mixture Models for Few-shot Semantic Segmentation\n",
    "SRN: Side-output Residual Network for Object Symmetry Detection in the Wild\n",
    "iBOT:Image BERT Pre-training with Online Tokenizer\n",
    "Mitigating the Effect of Incidental Correlations on Part-based Learning\n",
    "FINE-GRAINED FEW-SHOT RECOGNITION BY DEEP OBJECT PARSING\n",
    "Few-Shot Classification with Contrastive Learning\n",
    "Revisiting Prototypical Network for Cross Domain Few-Shot Learning\n",
    "Adversarial Feature Augmentation for Cross-domain Few-shot Classification\n",
    "When Does Self-supervision Improve Few-shot Learning?\n",
    "Supervised Masked Knowledge Distillation for Few-Shot Transformers\n",
    "Learning a Few-shot Embedding Model with Contrastive Learning\n",
    "RankDNN: Learning to Rank for Few-shot Learning\n",
    "ReSSL: Relational Self-Supervised Learning with Weak Augmentation\n",
    "Task-aware Part Mining Network for Few-Shot Learning\"\"\"\n",
    "ob_papers = file_df.loc[file_df['name'].str.contains('paper-'),'name']\n",
    "ob_papers = ob_papers.str.replace(\".md\", \"\").str.replace(\"paper-\", \"\")\n",
    "vika_papers, ob_papers = vika_papers.lower().replace(\":\",\"\"), ob_papers.str.lower().tolist()\n",
    "vika_papers = vika_papers.split('\\n')\n",
    "unregistered_paper = set(ob_papers) - set(vika_papers)\n",
    "len(ob_papers), len(vika_papers), len(unregistered_paper), sorted(list(unregistered_paper))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "model_id": "b6bb8446cdab4dd4aef5fd51b46c3c4b",
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       "version_minor": 0
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      "text/plain": [
       "  0%|          | 0/35 [00:00<?, ?it/s]"
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    "image.png (https://s1.vika.cn/space/2021/12/24/754732dd303b46d5a58b12e971b6adf8), image.png (https://s1.vika.cn/space/2021/12/24/01261e834aa74e7ca44a85b095c78620)\n",
    "image.png (https://s1.vika.cn/space/2022/03/07/a74b6b867eeb43128c55a6e713b95b51)\n",
    "image.png (https://s1.vika.cn/space/2022/02/17/4a185fdfb979448ab32508d124ea7f47)\n",
    "image.png (https://s1.vika.cn/space/2022/02/24/e4557b8cdc4c4336b679f729b91805eb)\n",
    "2021-11-13_21-05.png (https://s1.vika.cn/space/2021/11/13/34dc00afd5df42bbbd1bddca55374db0)\n",
    "image.png (https://s1.vika.cn/space/2022/02/23/89cd6d19565940ab8b8343f9074ad31e), image.png (https://s1.vika.cn/space/2022/02/23/1d29a3d9387d49c69e9b08f3d2c342c3), image.png (https://s1.vika.cn/space/2022/02/23/51d76ce1cfcb4e089fd7c9884bc4257e), image.png (https://s1.vika.cn/space/2022/02/23/cd9c627fc37f4806ad543ccc4e326ebd)\n",
    "image.png (https://s1.vika.cn/space/2021/12/06/53e13281ddd0453bbecd668aa466511b)\n",
    "image.png (https://s1.vika.cn/space/2022/02/16/224724d6c6d749fb9b22554d574e8b56), image.png (https://s1.vika.cn/space/2022/02/16/93bf4d6082f340f6989db1e47abb4b28), image.png (https://s1.vika.cn/space/2022/02/16/b0c0682c08ab43a094daa97ff11b28f0)\n",
    "2021-11-24_20-26.png (https://s1.vika.cn/space/2021/11/24/3b9aa630e654480c8642ba814b03ce2e)\n",
    "2021-11-24_15-37.png (https://s1.vika.cn/space/2021/11/24/6c7c8a0d9b704511a2ff7629215edaf5), 2021-11-24_16-44.png (https://s1.vika.cn/space/2021/11/24/1aff886669784150aed53750abe5057a)\n",
    "image.png (https://s1.vika.cn/space/2022/03/05/28941353617c4260813e959d6492bc20)\n",
    "image.png (https://s1.vika.cn/space/2022/03/02/eb81535c06d442858abc0a216f9baab8), image.png (https://s1.vika.cn/space/2022/03/02/c10348a4f056436d8645f5e66e38c66b), image.png (https://s1.vika.cn/space/2022/03/02/f6ffa5fef19a4cfb9996911968cffa55), image.png (https://s1.vika.cn/space/2022/03/02/1cd00f2203e14e13ab383e4a0dc7c4c5), image.png (https://s1.vika.cn/space/2022/03/02/7a3ac375df0a49a6885c0a4ccb7eb79d)\"\"\"\n",
    "vika_title = \"\"\"A Simple Framework for Contrastive Learning of Visual Representations\n",
    "A Two-phase Prototypical Network Model for Incremental Few-shot Relation Classification\n",
    "AugNLG: Few-shot Natural Language Generation using Self-trained Data Augmentation\n",
    "Big Self-Supervised Models are Strong Semi-Supervised Learners\n",
    "Class-Variant Margin Normalized Softmax Loss for Deep Face Recognition\n",
    "CosFace: Large Margin Cosine Loss for Deep Face Recognition\n",
    "Deep Pyramidal Residual Networks\n",
    "DER: Dynamically Expandable Representation for Class Incremental Learning\n",
    "Disentangling 3D Prototypical Networks for Few-Shot Concept Learning\n",
    "Distilling the Knowledge in a Neural Network\n",
    "Disturblabel: Regularizing cnn on the loss layer\n",
    "ElasticFace: Elastic Margin Loss for Deep Face Recognition\n",
    "Fair Loss: Margin-Aware Reinforcement Learning for Deep Face Recognition\n",
    "Few-shot Pseudo-Labeling for Intent Detection\n",
    "Few-Shot Text Classification with Triplet Networks, Data Augmentation, and Curriculum Learning\n",
    "Fisher vector faces in the wild\n",
    "Focal loss for dense object detection\n",
    "Heated-up softmax embedding\n",
    "Identity Mappings in Deep Residual Networks\n",
    "In Defense of the Triplet Loss for Person Re-Identification\n",
    "Learning to Compare: Relation Network for Few-Shot Learning\n",
    "MagFace: A Universal Representation for Face Recognition and Quality Assessment\n",
    "Matching networks for one shot learning\n",
    "Motion-supervised Co-Part Segmentation\n",
    "MS-Celeb-1M: A dataset and benchmark for large-scale face recognition\n",
    "Normface: L2 hypersphere embedding for face verification\n",
    "Range loss for deep face recognition with long-tailed training data\n",
    "Revisiting Self-Training for Few-Shot Learning of Language Model\n",
    "Ring Loss: Convex Feature Normalization for Face Recognition\n",
    "SCOPS: Self-Supervised Co-Part Segmentation\n",
    "Selective Kernel Networks\n",
    "Siamese neural networks for one-shot image recognition\n",
    "Simcse: Simple contrastive learning of sentence embbeddings\n",
    "The unreasonable effectiveness of noisy data for fine-grained recognition\n",
    "Training region-based object detectors with online hard example mining\"\"\"\n",
    "vika_img_url = vika_img_url.split('\\n')\n",
    "vika_img_url = [re.sub(\"([\\d\\-_]+?|image)\\.png \",\"\", url.replace(\"(\",'').replace(\")\",''),) for url in vika_img_url]\n",
    "vika_img_url = [url.replace(\"()\",'') for url in vika_img_url]\n",
    "vika_title = vika_title.split('\\n')\n",
    "\n",
    "headers = {\n",
    "        \"Connection\": \"keep-alive\",\n",
    "        \"sec-ch-ua\": '\"Chromium\";v=\"88\", \"Google Chrome\";v=\"88\", \";Not A Brand\";v=\"99\"',\n",
    "        \"sec-ch-ua-mobile\": \"?0\",\n",
    "        \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/88.0.4324.146 Safari/537.36\",\n",
    "        \"Accept\": \"image/avif,image/webp,image/apng,image/svg+xml,image/*,*/*;q=0.8\",\n",
    "        \"Sec-Fetch-Site\": \"same-site\",\n",
    "        \"Sec-Fetch-Mode\": \"no-cors\",\n",
    "        \"Sec-Fetch-Dest\": \"image\",\n",
    "        \"Accept-Language\": \"zh-CN,zh;q=0.9,en;q=0.8\",\n",
    "    }\n",
    "proxies = {\"http\": \"http://127.0.0.1:7890\", \"https\": \"http://127.0.0.1:7890\"}\n",
    "# warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "for title, url in tqdm(zip(vika_title, vika_img_url), total=len(vika_title)):\n",
    "    urls = url.split(',')\n",
    "    for i, u in enumerate(urls):\n",
    "        title = title.replace('?',\"\").replace(':',\" \")\n",
    "        file_name = os.path.join(r\"C:\\Users\\isidore\\Pictures\\Down\",title+\"#\"+str(i)+\".png\")\n",
    "        if os.path.exists(file_name):\n",
    "            continue\n",
    "        res = requests.get(u, headers=headers, timeout=3, proxies=proxies, verify=False)\n",
    "        with open(file_name, 'wb') as f:\n",
    "            f.write(res.content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['https://s1.vika.cn/space/2022/02/19/929597d1c59b4fc18d77e6622fc89919, https://s1.vika.cn/space/2022/02/19/a25882ae51d2481c89e81b9fade0277a, https://s1.vika.cn/space/2022/02/20/a6e133a4e1c34a12bcba5acaca9597d9',\n",
       " 'https://s1.vika.cn/space/2021/12/19/5823d0997b04417b8e4068380fa994cc, https://s1.vika.cn/space/2021/12/19/857f0d9da9bf40e0b9825a470f7fb9ee, https://s1.vika.cn/space/2021/12/19/2bb6d9ed43384599aeae572ea445e7eb, https://s1.vika.cn/space/2021/12/19/cd62a20b1801458695dd5424bd2d31fa, https://s1.vika.cn/space/2021/12/19/51018d128ad742ea88598f4ca0c195ff, https://s1.vika.cn/space/2021/12/19/b507e7890fe44b7b905d7a54cf1f1fdd, https://s1.vika.cn/space/2021/12/19/f266da7a03d741b58ca5eb3a3e05d4fd, https://s1.vika.cn/space/2021/12/19/7d7fb452c14844669ab99a62d8bc65d8, https://s1.vika.cn/space/2021/12/19/111c909351cf4b2aa8cc6bc0298890e2, https://s1.vika.cn/space/2021/12/19/5d02ba5ff3ec4aac917b1412c66fd157, https://s1.vika.cn/space/2021/12/20/35075b2916024f9ea677bba2cf6c70ec, https://s1.vika.cn/space/2021/12/20/12bf69154f944bcaa947dd656e747d69, https://s1.vika.cn/space/2021/12/20/c33bf333be424d109419b4684965c4c7',\n",
       " 'https://s1.vika.cn/space/2021/11/11/28d93bb327e14960a729a65cb37455cf, https://s1.vika.cn/space/2021/11/13/0313cda47a294be0a0956324baf835ff, https://s1.vika.cn/space/2021/11/16/a6e900db385a40609ee13878b7f71a84']"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vika_img_url[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def mv_items(items, src, dst):\n",
    "    for item in items:\n",
    "        shutil.move(item, item.replace(src, dst))\n",
    "\n",
    "src, dst = \"pages\", \"dataset\"\n",
    "# src, dst = \"noteasy\", \"\"\n",
    "items = file_df.loc[[len(re.findall(\"dataset-\", item))>0 for item in file_df['name'].tolist()], 'path']\n",
    "\n",
    "mv_items(items, src, dst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "972     C:\\Users\\isidore\\Nutstore\\1\\noteasy\\pages\\pape...\n",
       "973     C:\\Users\\isidore\\Nutstore\\1\\noteasy\\pages\\pape...\n",
       "974     C:\\Users\\isidore\\Nutstore\\1\\noteasy\\pages\\pape...\n",
       "975     C:\\Users\\isidore\\Nutstore\\1\\noteasy\\pages\\pape...\n",
       "976     C:\\Users\\isidore\\Nutstore\\1\\noteasy\\pages\\pape...\n",
       "                              ...                        \n",
       "1817    C:\\Users\\isidore\\Nutstore\\1\\noteasy\\pages\\view...\n",
       "1818    C:\\Users\\isidore\\Nutstore\\1\\noteasy\\pages\\view...\n",
       "1819    C:\\Users\\isidore\\Nutstore\\1\\noteasy\\pages\\view...\n",
       "1820    C:\\Users\\isidore\\Nutstore\\1\\noteasy\\pages\\view...\n",
       "1821    C:\\Users\\isidore\\Nutstore\\1\\noteasy\\pages\\view...\n",
       "Name: path, Length: 614, dtype: object"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "items"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "property_list = ['ac-lab','ac-pub']\n",
    "property_dict, property_sub_dict = get_property_dict(file_df, property_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ac-lab', 'ac-pub']"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "property_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['template-ac-dataset.md',\n",
       " 'template-ac-genre.md',\n",
       " 'template-ac-project.md',\n",
       " 'template-ac-publisher.md',\n",
       " 'template-ac-task.md',\n",
       " 'template-laboratory.md',\n",
       " 'template-paper.md',\n",
       " '1x1卷积.md',\n",
       " 'ac-genre-Data.md',\n",
       " 'ac-genre-diffusion.md',\n",
       " 'ac-genre-Digital Space.md',\n",
       " 'ac-genre-Generation.md',\n",
       " 'ac-genre-Industry.md',\n",
       " 'ac-genre-KG.md',\n",
       " 'ac-genre-logic.md',\n",
       " 'ac-genre-machine.md',\n",
       " 'ac-genre-MAN.md',\n",
       " 'ac-genre-math.md',\n",
       " 'ac-genre-Meta Semantic Learning.md',\n",
       " 'ac-genre-multimodal.md',\n",
       " 'ac-genre-Network.md',\n",
       " 'ac-genre-NLP.md',\n",
       " 'ac-genre-OPOB.md',\n",
       " 'ac-genre-prototypical networks.md',\n",
       " 'ac-genre-representation-space.md',\n",
       " 'ac-genre-representation-vector.md',\n",
       " 'ac-genre-setting.md',\n",
       " 'ac-genre-ViT.md',\n",
       " 'ac-genre-一致性.md',\n",
       " 'ac-genre-图网络.md',\n",
       " 'ac-genre-推荐系统.md',\n",
       " 'ac-genre-解构.md',\n",
       " 'ac-lab-4Paradigm.md',\n",
       " 'ac-lab-Adobe.md',\n",
       " 'ac-lab-Advanced Digital Sciences Center.md',\n",
       " 'ac-lab-Aibee.md',\n",
       " 'ac-lab-Alibaba Group.md',\n",
       " 'ac-lab-Amazon Alexa AI.md',\n",
       " 'ac-lab-Amazon Rekognition.md',\n",
       " 'ac-lab-Amazon Web Services.md',\n",
       " 'ac-lab-Ant Financial.md',\n",
       " 'ac-lab-ASAPP.md',\n",
       " 'ac-lab-Australian National Univerity.md',\n",
       " 'ac-lab-Australian National University.md',\n",
       " 'ac-lab-AZFT Joint Lab for Knowledge Engine.md',\n",
       " 'ac-lab-Baidu Research.md',\n",
       " 'ac-lab-Beihang University.md',\n",
       " 'ac-lab-Beijing Academy of Artificial Intelligence.md',\n",
       " 'ac-lab-Beijing Key Laboratory of Digital Media.md',\n",
       " 'ac-lab-Beijing Normal University.md',\n",
       " 'ac-lab-Beijing University of Posts and Telecommunications.md',\n",
       " 'ac-lab-Bell.md',\n",
       " 'ac-lab-ByteDance.md',\n",
       " 'ac-lab-California Institute of Technology.md',\n",
       " 'ac-lab-Canadian Institute for Advanced Research.md',\n",
       " 'ac-lab-Canon Information Technology.md',\n",
       " 'ac-lab-Carnegie Mellon University.md',\n",
       " 'ac-lab-CAS Center for Excellence of Brain Science and Intelligence Technology.md',\n",
       " 'ac-lab-CASIA-Chinese Academy of Sciences.md',\n",
       " 'ac-lab-CIFAR AI Research Chair.md',\n",
       " 'ac-lab-Columbia University.md',\n",
       " 'ac-lab-Cornell Tech.md',\n",
       " 'ac-lab-Cornell University.md',\n",
       " 'ac-lab-Dartmouth College.md',\n",
       " 'ac-lab-Data61.md',\n",
       " 'ac-lab-DeepMind.md',\n",
       " 'ac-lab-Department of Computer Science, Princeton University.md',\n",
       " 'ac-lab-Duke University.md',\n",
       " 'ac-lab-ECE-University of Illinois at Urbana-Champaign.md',\n",
       " 'ac-lab-Ecole Normale Superieure.md',\n",
       " 'ac-lab-EEE-Nanyang Technological University.md',\n",
       " 'ac-lab-Emory University.md',\n",
       " 'ac-lab-ERCE.md',\n",
       " 'ac-lab-Etsy Inc.md',\n",
       " 'ac-lab-Facebook AI Research.md',\n",
       " 'ac-lab-Fondazione Bruno Kessler.md',\n",
       " 'ac-lab-Fraunhofer Institute for Computer Graphics Research IGD.md',\n",
       " 'ac-lab-Georgia Institute of Technology.md',\n",
       " 'ac-lab-Google AI Language.md',\n",
       " 'ac-lab-Google Brain.md',\n",
       " 'ac-lab-Google Cloud AI.md',\n",
       " 'ac-lab-Google Research.md',\n",
       " 'ac-lab-Griffith University.md',\n",
       " 'ac-lab-Hebrew University of Jerusalem.md',\n",
       " 'ac-lab-HEC Montreal.md',\n",
       " 'ac-lab-Hefei University of Technology.md',\n",
       " 'ac-lab-Heidelberg University.md',\n",
       " 'ac-lab-Hong Kong Baptist University.md',\n",
       " 'ac-lab-Hong Kong University of Science and Technology.md',\n",
       " 'ac-lab-Huawei Inc.md',\n",
       " 'ac-lab-IBM Research.md',\n",
       " 'ac-lab-IDIAP.md',\n",
       " 'ac-lab-IIAI.md',\n",
       " 'ac-lab-Indian Institute of Science.md',\n",
       " 'ac-lab-Indian Institute of Technology.md',\n",
       " 'ac-lab-Inria.md',\n",
       " 'ac-lab-Institut Polytechnique de Paris.md',\n",
       " 'ac-lab-Institute for Advanced Study.md',\n",
       " 'ac-lab-Intellifusion.md',\n",
       " 'ac-lab-International Monetary Fund.md',\n",
       " 'ac-lab-JD Explore Academy.md',\n",
       " 'ac-lab-Johns Hopkins University.md',\n",
       " 'ac-lab-KAIST.md',\n",
       " 'ac-lab-Kakao Corp.md',\n",
       " 'ac-lab-Key Lab of Machine Perception.md',\n",
       " 'ac-lab-Kriston AI Lab.md',\n",
       " 'ac-lab-Laboratoire Hubert Curien.md',\n",
       " 'ac-lab-Massachusetts Institute of Technology.md',\n",
       " 'ac-lab-Mathematical and Applied Visual Computing, TU Darmstadt.md',\n",
       " 'ac-lab-McGill University.md',\n",
       " 'ac-lab-Meetic.md',\n",
       " 'ac-lab-Megvii Inc.md',\n",
       " 'ac-lab-MEGVII Technology.md',\n",
       " 'ac-lab-Michigan State University.md',\n",
       " 'ac-lab-Michigan.md',\n",
       " 'ac-lab-Microsoft AI.md',\n",
       " 'ac-lab-Microsoft Research Asia.md',\n",
       " 'ac-lab-Microsoft Research.md',\n",
       " 'ac-lab-MILA.md',\n",
       " 'ac-lab-MIT AI.md',\n",
       " 'ac-lab-MIT CSAIL.md',\n",
       " 'ac-lab-Nankai University.md',\n",
       " 'ac-lab-Nanyang Technological University.md',\n",
       " 'ac-lab-National Engineering Laboratory for Speech and Language Information Processing.md',\n",
       " 'ac-lab-National Research Council Canada.md',\n",
       " 'ac-lab-National University of Singapore.md',\n",
       " 'ac-lab-National Yang Ming Chiao Tung University.md',\n",
       " 'ac-lab-NCR Corporation.md',\n",
       " 'ac-lab-NEC Labs America.md',\n",
       " 'ac-lab-New York University.md',\n",
       " 'ac-lab-NLPR.md',\n",
       " 'ac-lab-Norwegian University of Science and Technology.md',\n",
       " 'ac-lab-NVIDIA.md',\n",
       " 'ac-lab-OpenAI.md',\n",
       " 'ac-lab-Orcam Ltd.md',\n",
       " 'ac-lab-Peking University.md',\n",
       " 'ac-lab-Peng Cheng Laboratory.md',\n",
       " 'ac-lab-Princeton University.md',\n",
       " 'ac-lab-ProtagoLabs.md',\n",
       " 'ac-lab-Purdue University.md',\n",
       " 'ac-lab-Qingao University.md',\n",
       " 'ac-lab-Queen Mary University of London.md',\n",
       " 'ac-lab-Rensselaer Polytechnic Institute.md',\n",
       " 'ac-lab-RIKEN Frontier Research Program.md',\n",
       " 'ac-lab-RWTH Aachen University.md',\n",
       " 'ac-lab-salesforce.md',\n",
       " 'ac-lab-Samsung R&D institute of China.md',\n",
       " 'ac-lab-SenseTime Joint Laboratory.md',\n",
       " 'ac-lab-SenseTime Research.md',\n",
       " 'ac-lab-Shandong University.md',\n",
       " 'ac-lab-Shanghai Engineering Research Center of Intelligent Vision and Imaging.md',\n",
       " 'ac-lab-Shanghai Jiao Tong University.md',\n",
       " 'ac-lab-ShanghaiTech University.md',\n",
       " 'ac-lab-Shenzhen Institutes of Advanced Technology.md',\n",
       " 'ac-lab-Singapore Management University.md',\n",
       " 'ac-lab-Snap.md',\n",
       " 'ac-lab-Sorbonne University.md',\n",
       " 'ac-lab-South China University of Technology Big Data and Intelligent Robot.md',\n",
       " 'ac-lab-South China University of Technology SE.md',\n",
       " 'ac-lab-South China University of Technology.md',\n",
       " 'ac-lab-Southern University of Science and Technology.md',\n",
       " 'ac-lab-Standford University.md',\n",
       " 'ac-lab-State Key Laboratory of Virtual Reality Technology and Systems.md',\n",
       " 'ac-lab-State University of New York at Buffalo.md',\n",
       " 'ac-lab-Stony Brook University.md',\n",
       " 'ac-lab-Sun Yat-sen University.md',\n",
       " 'ac-lab-Technical University of Darmstadt.md',\n",
       " 'ac-lab-Tel Aviv University.md',\n",
       " 'ac-lab-Telecom Paris.md',\n",
       " 'ac-lab-Tencent AI.md',\n",
       " 'ac-lab-Tencent Research.md',\n",
       " 'ac-lab-Texas A&M University.md',\n",
       " 'ac-lab-The Chinese University of Hong Kong (Shenzhen).md',\n",
       " 'ac-lab-The Chinese University of Hong Kong.md',\n",
       " 'ac-lab-The Hong Kong Polytechnic University.md',\n",
       " 'ac-lab-The Interaction Lab.md',\n",
       " 'ac-lab-The Pennsylvania State University.md',\n",
       " 'ac-lab-The University of Edinburgh.md',\n",
       " 'ac-lab-The University of Queensland.md',\n",
       " 'ac-lab-The Visual Defence Intelligence Network.md',\n",
       " 'ac-lab-Tsinghua AI.md',\n",
       " 'ac-lab-Tsinghua Auto.md',\n",
       " 'ac-lab-Tsinghua CS.md',\n",
       " 'ac-lab-Tsinghua IST.md',\n",
       " 'ac-lab-Tsinghua ITS.md',\n",
       " 'ac-lab-Tsinghua University Institute for Interdisciplinary Information Sciences.md',\n",
       " 'ac-lab-Tsinghua University.md',\n",
       " 'ac-lab-Twitter.md',\n",
       " 'ac-lab-UC Berkeley.md',\n",
       " 'ac-lab-Universidad de la Republica.md',\n",
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       " 'paper-Adversarial Graph Augmentation to Improve Graph Contrastive Learning.md',\n",
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       " 'paper-ALBERT A Lite BERT for Self-supervised Learning of Language Representations.md',\n",
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       " 'paper-Averaging weights leads to wider optima and better generalization.md',\n",
       " 'paper-Bag of tricks for image classification with convolutional neural networks.md',\n",
       " 'paper-Barlow Twins Self-Supervised Learning via Redundancy Reduction.md',\n",
       " 'paper-Batch normalization accelerating deep network training by reducing internal covariate shift.md',\n",
       " 'paper-BATCH NORMALIZATION EXPLAINED.md',\n",
       " 'paper-Batch-Instance Normalization for Adaptively Style-Invariant Neural Networks.md',\n",
       " 'paper-BatchFormer Learning to Explore Sample Relationships for Robust Representation Learning.md',\n",
       " 'paper-BatchFormerV2 Exploring Sample Relationships for Dense Representation Learning.md',\n",
       " 'paper-Benchmarking natural language understanding services for building conversational agents.md',\n",
       " 'paper-BERT Pre-training of Deep Bidirectional Transformers for Language Understanding.md',\n",
       " 'paper-Beyond Synthetic Noise Deep Learning on Controlled Noisy Labels.md',\n",
       " 'paper-Big Self-Supervised Models are Strong Semi-Supervised Learners.md',\n",
       " 'paper-Billion-scale similarity search with gpus.md',\n",
       " 'paper-Bootstrap latent-predictive representations for multitask reinforcement learning.md',\n",
       " 'paper-Bootstrap Your Own Latent A New Approach to Self-Supervised Learning.md',\n",
       " 'paper-Bottleneck Transformers for Visual Recognition.md',\n",
       " 'paper-Breaking the softmax bottleneck A high-rank rnn language model.md',\n",
       " 'paper-BroadFace Looking at Tens of Thousands of People at Once for Face Recognition.md',\n",
       " 'paper-Building a large annotated corpus of english The penn treebank.md',\n",
       " 'paper-Building a Question Answering Test Collection.md',\n",
       " 'paper-BYOL works even without batch statistics.md',\n",
       " 'paper-Caltech-UCSD Birds 200.md',\n",
       " 'paper-Cam-softmax for discriminative deep feature learning.md',\n",
       " 'paper-CAMA Class activation mapping disruptive attack for deep neural networks.md',\n",
       " 'paper-CamStyle A Novel Data Augmentation Method for Person Re-Identification.md',\n",
       " 'paper-Cascaded diffusion models for high fidelity image generation.md',\n",
       " 'paper-Categorizing nine visual classes using local appearance descriptors.md',\n",
       " 'paper-CenterNet Keypoint Triplets for Object Detection.md',\n",
       " 'paper-Circle Loss A Unified Perspective of Pair Similarity Optimization.md',\n",
       " 'paper-Class-Aware Contrastive Semi-Supervised Learning.md',\n",
       " 'paper-Class-Variant Margin Normalized Softmax Loss for Deep Face Recognition.md',\n",
       " 'paper-Classifying materials from images to cluster or not to cluster.md',\n",
       " 'paper-CLEVR A diagnostic dataset for compositional language and elementary visual reasoning.md',\n",
       " 'paper-Clinically applicable deep learning for diagnosis and referral in retinal disease.md',\n",
       " 'paper-Clustering with bregman divergences.md',\n",
       " 'paper-CMT Convolutional Neural Networks Meet Vision Transformers.md',\n",
       " 'paper-Co-Mining Deep Face Recognition With Noisy Labels.md',\n",
       " 'paper-CoAtNet Marrying Convolution and Attention for All Data Sizes.md',\n",
       " 'paper-Complex embeddings for simple link prediction.md',\n",
       " 'paper-Concept Generalization in Visual Representation Learning.md',\n",
       " 'paper-Conditional channel gated networks for task-aware continual learning.md',\n",
       " 'paper-Conformer Local Features Coupling Global Representations for Visual Recognition.md',\n",
       " 'paper-Constructing visual models with a latent space approach.md',\n",
       " 'paper-Contextual Transformer Networks for Visual Recognition.md',\n",
       " 'paper-Contrastive multiview coding.md',\n",
       " 'paper-Controllable and Guided Face Synthesis for Unconstrained Face Recognition.md',\n",
       " 'paper-ConViT Improving Vision Transformers with Soft Convolutional Inductive Biases.md',\n",
       " 'paper-Convolutional 2D Knowledge Graph Embeddings.md',\n",
       " 'paper-CosFace Large Margin Cosine Loss for Deep Face Recognition.md',\n",
       " 'paper-Cosine Normalization Using Cosine Similarity Instead of Dot Product in Neural Networks.md',\n",
       " 'paper-COSTA Covariance-Preserving Feature Augmentation for Graph Contrastive Learning.md',\n",
       " 'paper-CQA-Face Contrastive Quality-Aware Attentions for Face Recognition.md',\n",
       " 'paper-Cross-pose LFW  A database for studying cross-pose face recognition in unconstrained environments.md',\n",
       " 'paper-Cross-resolution learning for Face Recognition.md',\n",
       " 'paper-Crossage LFW A database for studying cross-age face recognition in unconstrained environments.md',\n",
       " 'paper-CrossCLR Cross-modal Contrastive Learning For Multi-modal Video Representations.md',\n",
       " 'paper-CurricularFace Adaptive Curriculum Learning Loss for Deep Face Recognition.md',\n",
       " 'paper-Curriculum Learning.md',\n",
       " 'paper-CvT Introducing Convolutions to Vision Transformers.md',\n",
       " 'paper-Data augmentation for face recognition.md',\n",
       " 'paper-Data Augmentation-Based Joint Learning for Heterogeneous Face Recognition.md',\n",
       " 'paper-Data-efficient image recognition with contrastive predictive coding.md',\n",
       " 'paper-Deblurring Face Images with Exemplars.md',\n",
       " 'paper-Decoupled networks.md',\n",
       " 'paper-Decoupled Weight Decay Regularization.md',\n",
       " 'paper-Deep clustering for unsupervised learning of visual features.md',\n",
       " 'paper-Deep Convolutional Neural Network Features and the Original Image.md',\n",
       " 'paper-Deep Face Recognition.md',\n",
       " 'paper-Deep features class activation map for thermal face detection and tracking.md',\n",
       " 'paper-Deep hashing for compact binary codes learning.md',\n",
       " 'paper-Deep Hyperspherical Learning.md',\n",
       " 'paper-Deep learning face representation by joint identification-verification.md',\n",
       " 'paper-Deep learning face representation from predicting 10,000 classes.md',\n",
       " 'paper-Deep learning is robust to massive label noise.md',\n",
       " 'paper-Deep Metric Learning via Lifted Structured Feature Embedding.md',\n",
       " 'paper-Deep metric transfer for label propagation with limited annotated data.md',\n",
       " 'paper-Deep nearest class mean classifiers.md',\n",
       " 'paper-Deep networks with stochastic depth.md',\n",
       " 'paper-Deep parametric continuous convolutional neural networks.md',\n",
       " 'paper-Deep Pyramidal Residual Networks.md',\n",
       " 'paper-Deep Residual Learning for Image Recognition.md',\n",
       " 'paper-Deep Semantic Face Deblurring.md',\n",
       " 'paper-DeepFace Closing the Gap to Human-Level Performance in Face Verification.md',\n",
       " 'paper-Deepfashion Powering Robust Clothes Recognition and Retrieval with Rich Annotations.md',\n",
       " 'paper-Deephash Getting regularization, depth and finetuning right.md',\n",
       " 'paper-Deeply learned face representations are sparse, selective, and robust.md',\n",
       " 'paper-Deformable DETR Deformable Transformers for End-to-End Object Detection.md',\n",
       " 'paper-Demystifying local vision transformer Sparse connectivity, weight sharing, and dynamic weight.md',\n",
       " 'paper-Dependency-Aware Attention Control for Unconstrained Face Recognition with Image Sets.md',\n",
       " 'paper-DER Dynamically Expandable Representation for Class Incremental Learning.md',\n",
       " 'paper-Detecting Camouflaged Object in Frequency Domain.md',\n",
       " 'paper-Detection of Event of Interest for Satellite Video Understanding.md',\n",
       " 'paper-Dimensionality reduction by learning an invariant mapping.md',\n",
       " 'paper-Discovering hidden factors of variation in deep networks.md',\n",
       " 'paper-Discrete energy on rectifiable sets.md',\n",
       " 'paper-Discriminative Learning of Local Image Descriptors.md',\n",
       " 'paper-Discriminative learning quadratic discriminant function for handwriting recognition.md',\n",
       " 'paper-Discriminative unsupervised feature learning with convolutional neural networks.md',\n",
       " 'paper-Discriminative unsupervised feature learning with exemplar convolutional neural networks.md',\n",
       " 'paper-Disentangled Representation Learning GAN for Pose-Invariant Face Recognition.md',\n",
       " 'paper-Disentangling 3D Prototypical Networks for Few-Shot Concept Learning.md',\n",
       " 'paper-Distance Metric Learning for Large Margin Nearest Neighbor Classification.md',\n",
       " 'paper-Distilling the Knowledge in a Neural Network.md',\n",
       " 'paper-Disturblabel Regularizing cnn on the loss layer.md',\n",
       " 'paper-Do better ImageNet models transfer better.md',\n",
       " 'paper-Do deep nets really need weight decay and dropout.md',\n",
       " 'paper-Do Normalization Layers in a Deep ConvNet Really Need to Be Distinct.md',\n",
       " 'paper-Do vision transformers see like convolutional neural networks.md',\n",
       " 'paper-DreamBooth Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation.md',\n",
       " 'paper-Dropout as Data Augmentation.md',\n",
       " 'paper-DVG-Face Dual Variational Generation for Heterogeneous Face Recognition.md',\n",
       " 'paper-Dyn-arcface dynamic additive angular margin loss for deep face recognition.md',\n",
       " 'paper-Dynamic few-shot visual learning without forgetting.md',\n",
       " 'paper-Early Convolutions Help Transformers See Better.md',\n",
       " 'paper-EASY – Ensemble Augmented-Shot Y-shaped Learning State-Of-The-Art Few-Shot Classification with Simple Ingredients.md',\n",
       " 'paper-EDA Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks.md',\n",
       " 'paper-Efficient backprop.md',\n",
       " 'paper-Efficient match kernels between sets of features for visual recognition.md',\n",
       " 'paper-ElasticFace Elastic Margin Loss for Deep Face Recognition.md',\n",
       " 'paper-Emerging Properties in Self-Supervised Vision Transformers.md',\n",
       " 'paper-End-to-end incremental learning.md',\n",
       " 'paper-End-to-End Object Detection with Transformers.md',\n",
       " 'paper-Ensemble Making Few-Shot Learning Stronger.md',\n",
       " 'paper-EQFace A Simple Explicit Quality Network for Face Recognition.md',\n",
       " 'paper-Evaluation of prototype learning algorithms for nearest-neighbor classifier in application to handwritten character recognition.md',\n",
       " 'paper-Explaining and harnessing adversarial examples.md',\n",
       " 'paper-Exploiting cloze-questions for few-shot text classification and natural language inference.md',\n",
       " 'paper-Exploiting generative models in discriminative classifiers.md',\n",
       " 'paper-Exploring Plain Vision Transformer Backbones for Object Detection.md',\n",
       " 'paper-Exploring Racial Bias within Face Recognition via per-subject Adversarially-Enabled Data Augmentation.md',\n",
       " 'paper-Exploring Simple Siamese Representation Learning.md',\n",
       " 'paper-Exploring the Equivalence of Siamese Self-Supervised Learning via A Unified Gradient Framework.md',\n",
       " 'paper-Exploring the Limits of Out-of-Distribution Detection.md',\n",
       " 'paper-f-gan Training generative neural samplers using variational divergence minimization.md',\n",
       " 'paper-Face description with local binary patterns Application to face recognition.md',\n",
       " 'paper-Face Recognition in Low Quality Images A Survey.md',\n",
       " 'paper-Face recognition in unconstrained videos with matched background similarity.md',\n",
       " 'paper-Face-Specific Data Augmentation for Unconstrained Face Recognition.md',\n",
       " 'paper-Facenet A unified embedding for face recognition and clustering.md',\n",
       " 'paper-Fair Loss Margin-Aware Reinforcement Learning for Deep Face Recognition.md',\n",
       " 'paper-Fast RCNN.md',\n",
       " 'paper-Fast Spatio-Temporal Residual Network for Video Super-Resolution.md',\n",
       " 'paper-Faster r-cnn Towards real-time object detection with region proposal networks.md',\n",
       " 'paper-Feature Aggregation Network for Video Face Recognition.md',\n",
       " 'paper-Feature pyramid networks for object detection.md',\n",
       " 'paper-Feature transfer learning for deep face recognition with under-represented data.md',\n",
       " 'paper-Few-shot and zero-shot multi-label learning for structured label spaces.md',\n",
       " 'paper-Few-Shot Emotion Recognition in Conversation with Sequential Prototypical Networks.md',\n",
       " 'paper-Few-shot learning with graph neural networks.md',\n",
       " 'paper-Few-shot natural language generation for task-oriented dialog.md',\n",
       " 'paper-Few-shot Pseudo-Labeling for Intent Detection.md',\n",
       " 'paper-Few-Shot Text Classification with Triplet Networks, Data Augmentation, and Curriculum Learning.md',\n",
       " 'paper-FewRel A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation.md',\n",
       " 'paper-First order motion model for image animation.md',\n",
       " 'paper-Fisher kernels on visual vocabularies for image categorization.md',\n",
       " 'paper-Fisher vector faces in the wild.md',\n",
       " 'paper-Fixing the train-test resolution discrepancy.md',\n",
       " 'paper-Fixmatch Simplifying semi-supervised learning with consistency and confidence.md',\n",
       " 'paper-Focal loss for dense object detection.md',\n",
       " 'paper-FocusFace Multi-task Contrastive Learning for Masked Face Recognition.md',\n",
       " 'paper-Formal limitations on the measurement of mutual information.md',\n",
       " 'paper-FOUR THINGS EVERYONE SHOULD KNOW TO IMPROVE BATCH NORMALIZATION.md',\n",
       " 'paper-Frame-wise Action Representations for Long Videos via Sequence Contrastive Learning.md',\n",
       " 'paper-From image descriptions to visual denotations New similarity metrics for semantic inference over event descriptions.md',\n",
       " 'paper-Frontal to profile face verification in the wild.md',\n",
       " 'paper-Fully Convolutional Networks for Semantic Segmentation.md',\n",
       " 'paper-Gaussian prototypical networks for few-shot learning on omniglot.md',\n",
       " 'paper-Generalizing Across Domains via Cross-Gradient Training.md',\n",
       " 'paper-Generalizing from a Few Examples A Survey on Few-shot Learning.md',\n",
       " 'paper-Generate to Adapt Resolution Adaption Network for Surveillance Face Recognition.md',\n",
       " 'paper-Generative adversarial nets.md',\n",
       " 'paper-Geometric representation of high dimension, low sample size data.md',\n",
       " 'paper-GIRAFFE representation Scenes as composition Generative Neural Feature Fields.md',\n",
       " 'paper-GIRAFFE Representing Scenes As Compositional Generative Neural Feature Fields.md',\n",
       " \"paper-GRACE Generating Concise and Informative Contrastive Sample to Explain Neural Network Model's Prediction.md\",\n",
       " 'paper-GRAF Generative Radiance Fields for 3D-Aware Image Synthesis.md',\n",
       " 'paper-Graph Contrastive Learning Automated.md',\n",
       " 'paper-Graph Contrastive Learning with Augmentations.md',\n",
       " 'paper-Group Normalization.md',\n",
       " 'paper-Habitat A Platform for Embodied AI Research.md',\n",
       " 'paper-Heated-up softmax embedding.md',\n",
       " 'paper-Herding dynamical weights to learn.md',\n",
       " 'paper-Hierarchical grouping to optimize an objective function.md',\n",
       " 'paper-Hierarchical relation extraction with coarse-to-fine grained attention..md',\n",
       " 'paper-Hierarchical text classification of Amazon product reviews.md',\n",
       " 'paper-Highly scalable deep learning training system with mixed-precision Training imagenet in four minutes.md',\n",
       " 'paper-Hologan Unsupervised learning of 3d representations from natural images.md',\n",
       " 'paper-How contextual are contextualized word representations comparing the geometry of BERT, ELMo, and GPT-2 embeddings.md',\n",
       " 'paper-How Does Batch Normalization Help Optimization.md',\n",
       " 'paper-How to fine-tune bert for text classification.md',\n",
       " 'paper-How to grow a mind.md',\n",
       " 'paper-Hubness and pollution Delving into cross-space mapping for zero-shot learning.md',\n",
       " 'paper-Huggingface’s transformers State-of-the-art natural language processing.md',\n",
       " 'paper-Human-level concept learning through probabilistic program induction.md',\n",
       " 'paper-Human-level control through deep reinforcement learning.md',\n",
       " 'paper-Hybrid attention-based prototypical networks for noisy few-shot relation classification.md',\n",
       " 'paper-HybridCR Weakly-Supervised 3D Point Cloud Semantic Segmentation via Hybrid Contrastive Regularization.md',\n",
       " 'paper-Hyperspherical Prototype Networks.md',\n",
       " 'paper-I-SECRET Importance-Guided Fundus Image Enhancement via Semi-supervised Contrastive Constraining.md',\n",
       " 'paper-IARPA janus benchmark - C face dataset and protocol.md',\n",
       " 'paper-IARPA janus benchmark-b face dataset.md',\n",
       " 'paper-icarl Incremental classifier and representation learning.md',\n",
       " 'paper-Identity Mappings in Deep Residual Networks.md',\n",
       " 'paper-Image retrieval by shape A comparative study.md',\n",
       " 'paper-ImageNet Large Scale Visual Recognition Challenge.md',\n",
       " 'paper-ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness.md',\n",
       " 'paper-Implicit semantic data augmentation for deep networks.md',\n",
       " 'paper-Improved Baselines with Momentum Contrastive Learning.md',\n",
       " 'paper-Improved deep metric learning with multi-class n-pair loss objective.md',\n",
       " 'paper-Improved Residual Networks for Image and Video Recognition.md',\n",
       " 'paper-Improving Answer Selection and Answer Triggering using Hard Negatives.md',\n",
       " 'paper-Improving Face Recognition from Hard Samples via Distribution Distillation Loss.md',\n",
       " 'paper-Improving the Fisher kernel for large-scale image classification.md',\n",
       " 'paper-Improving “bag-of-keypoints” image categorisation.md',\n",
       " 'paper-In Defense of the Triplet Loss for Person Re-Identification.md',\n",
       " 'paper-In Learning to learn.md',\n",
       " 'paper-Incorporating Convolution Designs into Visual Transformers.md',\n",
       " 'paper-Indoor segmentation and support inference from rgbd images.md',\n",
       " 'paper-Inflated episodic memory with region self-attention for long-tailed visual recognition.md',\n",
       " 'paper-InteractE Improving Convolution-Based Knowledge Graph Embeddings by Increasing Feature Interactions.md',\n",
       " 'paper-IntraLoss Further Margin via Gradient-Enhancing Term for Deep Face Recognition.md',\n",
       " 'paper-Intriguing Properties of Contrastive Losses.md',\n",
       " 'paper-Intriguing properties of neural networks.md',\n",
       " 'paper-Intrinsic Hyperspectral Image Decomposition With DSM Cues.md',\n",
       " 'paper-Intrinsic Image Recovery From Remote Sensing Hyperspectral Images.md',\n",
       " 'paper-Introduction to gaussian processes.md',\n",
       " 'paper-Involution Inverting the Inherence of Convolution for Visual Recognition.md',\n",
       " 'paper-It’s DONE Direct ONE-shot learning with Hebbian weight imprinting.md',\n",
       " 'paper-Joint embeddings of shapes and images via cnn image purification.md',\n",
       " 'paper-Joint-task Self-supervised Learning for Temporal Correspondence.md',\n",
       " 'paper-K-BERT Enabling Language Representation with Knowledge Graph.md',\n",
       " 'paper-Knowledge Evolution in Neural Networks.md',\n",
       " 'paper-Knowledge graph embedding with hierarchical relation structure.md',\n",
       " 'paper-Knowledgebased weak supervision for information extraction of overlapping relations.md',\n",
       " 'paper-L2 Regularization versus Batch and Weight Normalization.md',\n",
       " 'paper-L2-constrained softmax loss for discriminative face verification.md',\n",
       " 'paper-Labeled faces in the wild A database for studying face recognition in unconstrained environments.md',\n",
       " 'paper-Labeled Faces in the Wild Updates and New Reporting Procedures.md',\n",
       " 'paper-Language models are few-shot learners.md',\n",
       " 'paper-Large batch training of convolutional networks.md',\n",
       " 'paper-Large margin prototypical network for few-shot relation classification with fine-grained features.md',\n",
       " 'paper-Large-margin softmax loss for convolutional neural networks.md',\n",
       " 'paper-Large-scale Bisample Learning on ID Versus Spot Face Recognition.md',\n",
       " 'paper-Large-scale long-tailed recognition in an open world.md',\n",
       " 'paper-Learning a deep embedding model for zero-shot learning.md',\n",
       " 'paper-Learning a metric embedding for face recognition using the multibatch method.md',\n",
       " 'paper-Learning a unified classifier incrementally via rebalancing.md',\n",
       " 'paper-Learning and Example Selection for Object and Pattern Detection.md',\n",
       " 'paper-Learning category-specific mesh reconstruction from image collections.md',\n",
       " 'paper-Learning Continuous Image Representation with Local Implicit Image Function.md',\n",
       " 'paper-Learning Deep Features for Discriminative Localization.md',\n",
       " 'paper-Learning deep representations by mutual information estimation and maximization.md',\n",
       " 'paper-Learning deep representations of fine-grained visual descriptions.md',\n",
       " 'paper-Learning Discriminative Aggregation Network for Video-Based Face Recognition.md',\n",
       " 'paper-Learning face representation from scratch.md',\n",
       " 'paper-Learning from unlabelled videos with contrastive predictive neural 3d mapping.md',\n",
       " 'paper-Learning in High Dimension Always Amounts to Extrapolation.md',\n",
       " 'paper-Learning Local Feature Descriptors Using Convex Optimisation.md',\n",
       " 'paper-Learning local feature descriptors with triplets and shallow convolutional neural networks.md',\n",
       " 'paper-Learning local image descriptors with deep siamese and triplet convolutional networks by minimising global loss functions.md',\n",
       " 'paper-Learning Multiple Adverse Weather Removal via Two-stage Knowledge Learning and Multi-contrastive Regularization Toward a Unified Model.md',\n",
       " 'paper-Learning representations by maximizing mutual information across views.md',\n",
       " 'paper-Learning Robust Global Representations by Penalizing Local Predictive Power.md',\n",
       " 'paper-Learning spatial common sense with geometry-aware recurrent networks.md',\n",
       " 'paper-Learning Spread-Out Local Feature Descriptors.md',\n",
       " 'paper-Learning Structured Embeddings of Knowledge Bases.md',\n",
       " 'paper-Learning to Compare Relation Network for Few-Shot Learning.md',\n",
       " 'paper-Learning to Detect Every Thing in an Open World.md',\n",
       " 'paper-Learning to learn by gradient descent by gradient descent.md',\n",
       " 'paper-Learning to Resize Images for Computer Vision Tasks.md',\n",
       " 'paper-Learning towards minimum hyperspherical energy.md',\n",
       " 'paper-Learning transformations for clustering and classification.md',\n",
       " 'paper-Local relation networks for image recognition.md',\n",
       " 'paper-LocalDrop A Hybrid Regularization for Deep Neural Networks.md',\n",
       " 'paper-Locality Guidance for Improving Vision Transformers on Tiny Datasets.md',\n",
       " 'paper-Long-tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution Networks.md',\n",
       " 'paper-Low-resolution face alignment and recognition using mixed-resolution classifiers.md',\n",
       " 'paper-Low-Shot Learning With Imprinted Weights.md',\n",
       " 'paper-MagFace A Universal Representation for Face Recognition and Quality Assessment.md',\n",
       " 'paper-Making Convolutional Networks Shift-Invariant Again.md',\n",
       " 'paper-Making pre-trained language models better few-shot learners.md',\n",
       " 'paper-MARS A Video Benchmark for Large-Scale Person Re-Identification.md',\n",
       " 'paper-Masked Autoencoders Are Scalable Vision Learners.md',\n",
       " 'paper-Matching networks for one shot learning.md',\n",
       " 'paper-Mean teachers are better role models Weight-averaged consistency targets improve semi-supervised deep learning results.md',\n",
       " 'paper-Memory-Augmented Relation Network for Few-Shot Learning.md',\n",
       " 'paper-MemoryBased Neighbourhood Embedding for Visual Recognition.md',\n",
       " 'paper-Mentornet Learning data-driven curriculum for very deep neural networks on corrupted labels.md',\n",
       " 'paper-Meta-learning for semi-supervised few-shot classification.md',\n",
       " 'paper-Metric learning with adaptive density discrimination.md',\n",
       " 'paper-MIND-Net A Deep Mutual Information Distillation Network for Realistic Low-Resolution Face Recognition.md',\n",
       " 'paper-Mine mutual information neural estimation.md',\n",
       " 'paper-Mining and summarizing customer reviews.md',\n",
       " 'paper-Mis-classified Vector Guided Softmax Loss for Face Recognition.md',\n",
       " 'paper-mixup Beyond empirical risk minimization.md',\n",
       " 'paper-MLP-Mixer An all-MLP Architecture for Vision.md',\n",
       " 'paper-Mobile-Former Bridging MobileNet and Transformer.md',\n",
       " 'paper-MobileNets Efficient Convolutional Neural Networks for Mobile Vision Applications.md',\n",
       " 'paper-MobileNetV2 Inverted Residuals and Linear Bottlenecks.md',\n",
       " 'paper-Modeling Relation Paths for Representation Learning of Knowledge Bases.md',\n",
       " 'paper-Modeling relations and their mentions without labeled text.md',\n",
       " 'paper-Modelling Uncertainty in Representation of Facial Features for Face Recognition.md',\n",
       " 'paper-Momentum contrast for unsupervised visual representation learning.md',\n",
       " 'paper-Motion-supervised Co-Part Segmentation.md',\n",
       " 'paper-MS-Celeb-1M A dataset and benchmark for large-scale face recognition.md',\n",
       " 'paper-Multi-Level Matching and Aggregation Network for Few-Shot Relation Classification.md',\n",
       " 'paper-MULTI-TEMPORAL SAR IMAGE DESPECKLING BASED A CONVOLUTIONAL NEURAL NETWORK.md',\n",
       " 'paper-Multicolumn Networks for Face Recognition.md',\n",
       " 'paper-Multidimensional Scaling for Matching Low-Resolution Face Images.md',\n",
       " 'paper-Multimodal hyperspectral remote sensing an overview and perspective.md',\n",
       " 'paper-Multiscale Vision Transformers.md',\n",
       " 'paper-Multiwoz-a large-scale multi-domain wizard-of-oz dataset for task-oriented dialogue modelling.md',\n",
       " 'paper-MViTv2 Improved Multiscale Vision Transformers for Classification and Detection.md',\n",
       " 'paper-Naive-deep face recognition Touching the limit of lfw benchmark or not.md',\n",
       " 'paper-Natural gradient works efficiently in learning.md',\n",
       " 'paper-Natural neural networks.md',\n",
       " 'paper-Neighbourhood component analysis.md',\n",
       " 'paper-NeRF Representing Scenes as Neural Radiance Fields for View Synthesis.md',\n",
       " 'paper-Neural aggregation network for video face recognition.md',\n",
       " 'paper-News Category Dataset.md',\n",
       " 'paper-Next-ViT Next Generation Vision Transformer for Efficient Deployment in Realistic Industrial Scenarios.md',\n",
       " 'paper-Noise-contrastive estimation A new estimation principle for unnormalized statistical models.md',\n",
       " 'paper-Noise-Tolerant Paradigm for Training Face Recognition CNNs.md',\n",
       " 'paper-Noisy activation functions.md',\n",
       " 'paper-Noisy softmax Improving the generalization ability of DCNN via postponing the early softmax saturation.md',\n",
       " 'paper-Non-local manifold parzen windows.md',\n",
       " 'paper-Non-local neural networks.md',\n",
       " 'paper-Normface L2 hypersphere embedding for face verification.md',\n",
       " 'paper-NPT-Loss Demystifying face recognition losses with Nearest Proxies Triplet.md',\n",
       " 'paper-Objects as Points.md',\n",
       " 'paper-Ole Orthogonal low-rank embedding-a plug and play geometric loss for deep learning.md',\n",
       " 'paper-On feature normalization and data augmentation.md',\n",
       " 'paper-On largebatch training for deep learning Generalization gap and sharp minima.md',\n",
       " 'paper-On Low-Resolution Face Recognition in the Wild Comparisons and New Techniques.md',\n",
       " 'paper-On mutual information maximization for representation learning.md',\n",
       " 'paper-On sampling strategies for neural networkbased collaborative filtering.md',\n",
       " 'paper-On the Integration of Self-Attention and Convolution.md',\n",
       " 'paper-On the Periodic Behavior of Neural Network Training with Batch Normalization and Weight Decay.md',\n",
       " 'paper-On the relationship between selfattention and convolutional layers.md',\n",
       " 'paper-On the trace and the sum of elements of a matrix.md',\n",
       " 'paper-On variational bounds of mutual information.md',\n",
       " 'paper-One shot learning of simple visual concepts.md',\n",
       " 'paper-One-shot face recognition by promoting underrepresented classes.md',\n",
       " 'paper-Online Adaptation for Consistent Mesh Reconstruction in the Wild.md',\n",
       " 'paper-Optimization as a model for few-shot learning.md',\n",
       " 'paper-Order matters Sequence to sequence for sets.md',\n",
       " 'paper-Orthogonality Loss Learning Discriminative Representations for Face Recognition.md',\n",
       " 'paper-Overcoming catastrophic forgetting with hard attention to the task.md',\n",
       " 'paper-P2SGrad Refined Gradients for Optimizing Deep Face Models.md',\n",
       " 'paper-ParaNMT50M Pushing the limits of paraphrastic sentence embeddings with millions of machine translations.md',\n",
       " 'paper-ParC-Net Position Aware Circular Convolution with Merits from ConvNets and Transformer.md',\n",
       " 'paper-PARN Position-Aware Relation Networks for Few-Shot Learning.md',\n",
       " 'paper-Pattern Analysis and Machine Intelligence.md',\n",
       " 'paper-Perceptual Losses for Real-Time Style Transfer and Super-Resolution.md',\n",
       " 'paper-Perceptual straightening of natural videos.md',\n",
       " 'paper-Person re-identification in the wild.md',\n",
       " 'paper-Perturbed Self-Distillation Weakly Supervised Large-Scale Point Cloud Semantic Segmentation.md',\n",
       " 'paper-pixelNeRF Neural Radiance Fields From One or Few Images.md',\n",
       " 'paper-Podnet Pooled outputs distillation for small-tasks incremental learning.md',\n",
       " 'paper-PointAugment an Auto-Augmentation Framework for Point Cloud Classification.md',\n",
       " 'paper-Popular nearest neighbors in high-dimensional data.md',\n",
       " 'paper-Probabilistic Elastic Matching for Pose Variant Face Verification.md',\n",
       " 'paper-Probabilistic Face Embeddings.md',\n",
       " 'paper-Progressive Reconstruction of Visual Structure for Image Inpainting.md',\n",
       " 'paper-PROTOTYPICAL CONTRASTIVE LEARNING OF UNSUPERVISED REPRESENTATIONS.md',\n",
       " 'paper-Prototypical Networks for Few-shot Learning.md',\n",
       " 'paper-Qamface Quadratic Additive Angular Margin Loss For Face Recognition.md',\n",
       " 'paper-QMagFace Simple and Accurate Quality-Aware Face Recognition.md',\n",
       " 'paper-Quality Aware Network for Set to Set Recognition.md',\n",
       " 'paper-Quaternion Knowledge Graph Embeddings.md',\n",
       " 'paper-R-SVM+ Robust Learning with Privileged Information.md',\n",
       " 'paper-Random path selection for continual learning.md',\n",
       " 'paper-Range loss for deep face recognition with long-tailed training data.md',\n",
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       "  '[[ac-lab-South China University of Technology SE]]': ['paper-A Two-phase Prototypical Network Model for Incremental Few-shot Relation Classification.md'],\n",
       "  '[[ac-lab-South China University of Technology Big Data and Intelligent Robot]]': ['paper-A Two-phase Prototypical Network Model for Incremental Few-shot Relation Classification.md'],\n",
       "  '[[ac-lab-The Hong Kong Polytechnic University]]': ['paper-A Two-phase Prototypical Network Model for Incremental Few-shot Relation Classification.md'],\n",
       "  '[[ac-lab-CASIA-Chinese Academy of Sciences]]': ['paper-Learning face representation from scratch.md'],\n",
       "  '[[ac-lab-The Chinese University of Hong Kong]]': ['paper-Unsupervised feature learning via non-parametric instance discrimination.md'],\n",
       "  '[[ac-lab-SenseTime Joint Laboratory]]': ['paper-AdaCos Adaptively Scaling Cosine Logits for Effectively Learning Deep Face Representations.md'],\n",
       "  '[[ac-lab-OpenAI]]': ['paper-Adversarial examples in the physical world.md'],\n",
       "  '[[ac-lab-Google Research]]': ['paper-Training highly multiclass classifiers.md'],\n",
       "  '[[ac-lab-University of Toronto]]': ['paper-Prototypical Networks for Few-shot Learning.md'],\n",
       "  '[[ac-lab-The Interaction Lab]]': ['paper-AugNLG Few-shot Natural Language Generation using Self-trained Data Augmentation.md'],\n",
       "  '[[ac-lab-Amazon Alexa AI]]': ['paper-AugNLG Few-shot Natural Language Generation using Self-trained Data Augmentation.md'],\n",
       "  '[[ac-lab-Amazon Web Services]]': ['paper-Bag of tricks for image classification with convolutional neural networks.md'],\n",
       "  '[[ac-lab-Cornell University]]': ['paper-Beyond Synthetic Noise Deep Learning on Controlled Noisy Labels.md'],\n",
       "  '[[ac-lab-Google Cloud AI]]': ['paper-Beyond Synthetic Noise Deep Learning on Controlled Noisy Labels.md'],\n",
       "  '[[ac-lab-Kakao Corp]]': ['paper-BroadFace Looking at Tens of Thousands of People at Once for Face Recognition.md'],\n",
       "  '[[ac-lab-University of Dundee]]': ['paper-Cam-softmax for discriminative deep feature learning.md'],\n",
       "  '[[ac-lab-Beihang University]]': ['paper-Circle Loss A Unified Perspective of Pair Similarity Optimization.md'],\n",
       "  '[[ac-lab-MEGVII Technology]]': ['paper-Circle Loss A Unified Perspective of Pair Similarity Optimization.md'],\n",
       "  '[[ac-lab-Tsinghua University]]': ['paper-MARS A Video Benchmark for Large-Scale Person Re-Identification.md'],\n",
       "  '[[ac-lab-Australian National Univerity]]': ['paper-Circle Loss A Unified Perspective of Pair Similarity Optimization.md'],\n",
       "  '[[ac-lab-University of Oxford]]': ['paper-Quaternion Knowledge Graph Embeddings.md'],\n",
       "  '[[ac-lab-ERCE]]': ['paper-Improving the Fisher kernel for large-scale image classification.md'],\n",
       "  '[[ac-lab-University College London]]': ['paper-Convolutional 2D Knowledge Graph Embeddings.md'],\n",
       "  '[[ac-lab-Universit ́ e Grenoble Alpes]]': ['paper-Complex embeddings for simple link prediction.md'],\n",
       "  '[[ac-lab-IDIAP]]': ['paper-Constructing visual models with a latent space approach.md'],\n",
       "  '[[ac-lab-Università della Svizzera italiana]]': ['paper-Convolutional 2D Knowledge Graph Embeddings.md'],\n",
       "  '[[ac-lab-Tencent AI]]': ['paper-Long-tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution Networks.md'],\n",
       "  '[[ac-lab-DeepMind]]': ['paper-Matching networks for one shot learning.md'],\n",
       "  '[[ac-lab-University of California]]': ['paper-On sampling strategies for neural networkbased collaborative filtering.md'],\n",
       "  '[[ac-lab-Visual Geometry Group Department of Engineering Science University of Oxford]]': ['paper-Fisher vector faces in the wild.md'],\n",
       "  '[[ac-lab-Tsinghua Auto]]': ['paper-Deep hashing for compact binary codes learning.md'],\n",
       "  '[[ac-lab-Advanced Digital Sciences Center]]': ['paper-Deep hashing for compact binary codes learning.md'],\n",
       "  '[[ac-lab-EEE-Nanyang Technological University]]': ['paper-Deep hashing for compact binary codes learning.md'],\n",
       "  '[[ac-lab-ECE-University of Illinois at Urbana-Champaign]]': ['paper-Deep hashing for compact binary codes learning.md'],\n",
       "  '[[ac-lab-Shenzhen Institutes of Advanced Technology]]': ['paper-P2SGrad Refined Gradients for Optimizing Deep Face Models.md'],\n",
       "  '[[ac-lab-Standford University]]': ['paper-The unreasonable effectiveness of noisy data for fine-grained recognition.md'],\n",
       "  '[[ac-lab-MIT CSAIL]]': ['paper-Understanding contrastive representation learning through alignment and uniformity on the hypersphere.md'],\n",
       "  '[[ac-lab-KAIST]]': ['paper-Deep Pyramidal Residual Networks.md'],\n",
       "  '[[ac-lab-Microsoft AI]]': ['paper-One-shot face recognition by promoting underrepresented classes.md'],\n",
       "  '[[ac-lab-Facebook AI Research]]': ['paper-Training region-based object detectors with online hard example mining.md'],\n",
       "  '[[ac-lab-Tel Aviv University]]': ['paper-DeepFace Closing the Gap to Human-Level Performance in Face Verification.md'],\n",
       "  '[[ac-lab-ShanghaiTech University]]': ['paper-DER Dynamically Expandable Representation for Class Incremental Learning.md'],\n",
       "  '[[ac-lab-University of Chinese Academy of Sciences]]': ['paper-Robust classification with convolutional prototype learning.md'],\n",
       "  '[[ac-lab-Shanghai Engineering Research Center of Intelligent Vision and Imaging]]': ['paper-DER Dynamically Expandable Representation for Class Incremental Learning.md'],\n",
       "  '[[ac-lab-Carnegie Mellon University]]': ['paper-Training region-based object detectors with online hard example mining.md'],\n",
       "  '[[ac-lab-University of Montreal]]': ['paper-Disentangling 3D Prototypical Networks for Few-Shot Concept Learning.md'],\n",
       "  '[[ac-lab-University of Pennsylvania]]': ['paper-Distance Metric Learning for Large Margin Nearest Neighbor Classification.md'],\n",
       "  '[[ac-lab-Universite de Montreal]]': ['paper-Dropout as Data Augmentation.md'],\n",
       "  '[[Goethe University Frankfurt]]': ['paper-Dropout as Data Augmentation.md'],\n",
       "  '[[ac-lab-Fraunhofer Institute for Computer Graphics Research IGD]]': ['paper-ElasticFace Elastic Margin Loss for Deep Face Recognition.md'],\n",
       "  '[[ac-lab-Mathematical and Applied Visual Computing': ['paper-ElasticFace Elastic Margin Loss for Deep Face Recognition.md'],\n",
       "  '[[ac-lab- Darmstadt]]': ['paper-ElasticFace Elastic Margin Loss for Deep Face Recognition.md'],\n",
       "  '[[ac-lab-MIT AI]]': ['paper-Metric learning with adaptive density discrimination.md'],\n",
       "  '[[ac-lab-Beijing University of Posts and Telecommunications]]': ['paper-Fair Loss Margin-Aware Reinforcement Learning for Deep Face Recognition.md'],\n",
       "  '[[ac-lab-Canon Information Technology]]': ['paper-Fair Loss Margin-Aware Reinforcement Learning for Deep Face Recognition.md'],\n",
       "  '[[ac-lab-Laboratoire Hubert Curien]]': ['paper-Few-shot Pseudo-Labeling for Intent Detection.md'],\n",
       "  '[[ac-lab-Meetic]]': ['paper-Few-shot Pseudo-Labeling for Intent Detection.md'],\n",
       "  '[[ac-lab-ProtagoLabs]]': ['paper-Few-Shot Text Classification with Triplet Networks, Data Augmentation, and Curriculum Learning.md'],\n",
       "  '[[ac-lab-International Monetary Fund]]': ['paper-Few-Shot Text Classification with Triplet Networks, Data Augmentation, and Curriculum Learning.md'],\n",
       "  '[[ac-lab-Dartmouth College]]': ['paper-Few-Shot Text Classification with Triplet Networks, Data Augmentation, and Curriculum Learning.md'],\n",
       "  '[[ac-lab-UC Berkeley]]': ['paper-Unsupervised feature learning via non-parametric instance discrimination.md'],\n",
       "  '[[ac-lab-Hong Kong University of Science and Technology]]': ['paper-Generalizing from a Few Examples A Survey on Few-shot Learning.md'],\n",
       "  '[[ac-lab-Baidu Research]]': ['paper-Large margin prototypical network for few-shot relation classification with fine-grained features.md'],\n",
       "  '[[ac-lab-4Paradigm]]': ['paper-Generalizing from a Few Examples A Survey on Few-shot Learning.md'],\n",
       "  '[[ac-lab-Columbia University]]': ['paper-Learning Spread-Out Local Feature Descriptors.md'],\n",
       "  '[[ac-lab-Tsinghua CS]]': ['paper-Rule-Guided Compositional Representation Learning on Knowledge Graphs.md'],\n",
       "  '[[ac-lab-Tsinghua ITS]]': ['paper-Modeling Relation Paths for Representation Learning of Knowledge Bases.md'],\n",
       "  '[[ac-lab-Tsinghua AI]]': ['paper-Hybrid attention-based prototypical networks for noisy few-shot relation classification.md'],\n",
       "  '[[ac-lab-Michigan]]': ['paper-ImageNet Large Scale Visual Recognition Challenge.md'],\n",
       "  '[[ac-lab-Massachusetts Institute of Technology]]': ['paper-ImageNet Large Scale Visual Recognition Challenge.md'],\n",
       "  '[[UNC Chapel Hill]]': ['paper-ImageNet Large Scale Visual Recognition Challenge.md'],\n",
       "  '[[ac-lab-IIAI]]': ['paper-Improved Residual Networks for Image and Video Recognition.md'],\n",
       "  '[[ac-lab-University of Southampton]]': ['paper-Improving “bag-of-keypoints” image categorisation.md'],\n",
       "  '[[ac-lab-RWTH Aachen University]]': ['paper-In Defense of the Triplet Loss for Person Re-Identification.md'],\n",
       "  '[[ac-lab-Indian Institute of Science]]': ['paper-InteractE Improving Convolution-Based Knowledge Graph Embeddings by Increasing Feature Interactions.md'],\n",
       "  '[[ac-lab-NVIDIA]]': ['paper-Self-supervised viewpoint learning from image collections.md'],\n",
       "  '[[ac-lab-Peking University]]': ['paper-MARS A Video Benchmark for Large-Scale Person Re-Identification.md'],\n",
       "  '[[ac-lab-Tencent Research]]': ['paper-K-BERT Enabling Language Representation with Knowledge Graph.md'],\n",
       "  '[[ac-lab-Beijing Normal University]]': ['paper-K-BERT Enabling Language Representation with Knowledge Graph.md'],\n",
       "  '[[ac-lab-University of Massachusetts Amherst]]': ['paper-Labeled Faces in the Wild Updates and New Reporting Procedures.md'],\n",
       "  '[[ac-lab-Stony Brook University]]': ['paper-Labeled faces in the wild A database for studying face recognition in unconstrained environments.md'],\n",
       "  '[[ac-lab-Queen Mary University of London]]': ['paper-Learning to Compare Relation Network for Few-Shot Learning.md'],\n",
       "  '[[ac-lab-Orcam Ltd]]': ['paper-Learning a metric embedding for face recognition using the multibatch method.md'],\n",
       "  '[[ac-lab-Microsoft Research]]': ['paper-MARS A Video Benchmark for Large-Scale Person Re-Identification.md'],\n",
       "  '[[ac-lab-The University of Edinburgh]]': ['paper-Learning to Compare Relation Network for Few-Shot Learning.md'],\n",
       "  '[[ac-lab-Georgia Institute of Technology]]': ['paper-Learning towards minimum hyperspherical energy.md'],\n",
       "  '[[ac-lab-Emory University]]': ['paper-Learning towards minimum hyperspherical energy.md'],\n",
       "  '[[ac-lab-South China University of Technology]]': ['paper-Learning towards minimum hyperspherical energy.md'],\n",
       "  '[[ac-lab-Ant Financial]]': ['paper-Learning towards minimum hyperspherical energy.md'],\n",
       "  '[[ac-lab-Duke University]]': ['paper-Ole Orthogonal low-rank embedding-a plug and play geometric loss for deep learning.md'],\n",
       "  '[[ac-lab-Zhejiang University]]': ['paper-Long-tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution Networks.md'],\n",
       "  '[[ac-lab-Alibaba Group]]': ['paper-Long-tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution Networks.md'],\n",
       "  '[[ac-lab-AZFT Joint Lab for Knowledge Engine]]': ['paper-Long-tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution Networks.md'],\n",
       "  '[[ac-lab-Adobe]]': ['paper-Making Convolutional Networks Shift-Invariant Again.md'],\n",
       "  '[[ac-lab-UTSA]]': ['paper-MARS A Video Benchmark for Large-Scale Person Re-Identification.md'],\n",
       "  '[[ac-lab-Tsinghua IST]]': ['paper-Modeling Relation Paths for Representation Learning of Knowledge Bases.md'],\n",
       "  '[[ac-lab-Samsung R&D institute of China]]': ['paper-Modeling Relation Paths for Representation Learning of Knowledge Bases.md'],\n",
       "  '[[ac-lab-University of Trento]]': ['paper-Motion-supervised Co-Part Segmentation.md'],\n",
       "  '[[ac-lab-Fondazione Bruno Kessler]]': ['paper-Motion-supervised Co-Part Segmentation.md'],\n",
       "  '[[ac-lab-Institut Polytechnique de Paris]]': ['paper-Motion-supervised Co-Part Segmentation.md'],\n",
       "  '[[ac-lab-Snap]]': ['paper-Motion-supervised Co-Part Segmentation.md'],\n",
       "  '[[ac-lab-Telecom Paris]]': ['paper-Motion-supervised Co-Part Segmentation.md'],\n",
       "  '[[ac-lab-National Engineering Laboratory for Speech and Language Information Processing]]': ['paper-Multi-Level Matching and Aggregation Network for Few-Shot Relation Classification.md'],\n",
       "  '[[ac-lab-Megvii Inc]]': ['paper-Naive-deep face recognition Touching the limit of lfw benchmark or not.md'],\n",
       "  '[[ac-lab-RIKEN Frontier Research Program]]': ['paper-Natural gradient works efficiently in learning.md'],\n",
       "  '[[ac-lab-University of Electronic Science and Technology of China]]': ['paper-Normface L2 hypersphere embedding for face verification.md'],\n",
       "  '[[ac-lab-Johns Hopkins University]]': ['paper-Normface L2 hypersphere embedding for face verification.md'],\n",
       "  '[[ac-lab-Universidad de la Republica]]': ['paper-Ole Orthogonal low-rank embedding-a plug and play geometric loss for deep learning.md'],\n",
       "  '[[ac-lab-Yahoo Research]]': ['paper-On sampling strategies for neural networkbased collaborative filtering.md'],\n",
       "  '[[ac-lab-Etsy Inc]]': ['paper-On sampling strategies for neural networkbased collaborative filtering.md'],\n",
       "  '[[ac-lab-Twitter]]': ['paper-Prototypical Networks for Few-shot Learning.md'],\n",
       "  '[[ac-lab-SenseTime Research]]': ['paper-P2SGrad Refined Gradients for Optimizing Deep Face Models.md'],\n",
       "  '[[ac-lab-Canadian Institute for Advanced Research]]': ['paper-Prototypical Networks for Few-shot Learning.md'],\n",
       "  '[[ac-lab-Key Lab of Machine Perception]]': ['paper-Qamface Quadratic Additive Angular Margin Loss For Face Recognition.md'],\n",
       "  '[[ac-lab-Nanyang Technological University]]': ['paper-Quaternion Knowledge Graph Embeddings.md'],\n",
       "  '[[ac-lab-National University of Singapore]]': ['paper-Revisiting Self-Training for Few-Shot Learning of Language Model.md'],\n",
       "  '[[ac-lab-Southern University of Science and Technology]]': ['paper-Revisiting Self-Training for Few-Shot Learning of Language Model.md'],\n",
       "  '[[ac-lab-The Chinese University of Hong Kong (Shenzhen)]]': ['paper-Revisiting Self-Training for Few-Shot Learning of Language Model.md'],\n",
       "  '[[ac-lab-Kriston AI Lab]]': ['paper-Revisiting Self-Training for Few-Shot Learning of Language Model.md'],\n",
       "  '[[ac-lab-NLPR]]': ['paper-Robust classification with convolutional prototype learning.md'],\n",
       "  '[[ac-lab-CAS Center for Excellence of Brain Science and Intelligence Technology]]': ['paper-Robust classification with convolutional prototype learning.md'],\n",
       "  '[[ac-lab-Beijing Key Laboratory of Digital Media]]': ['paper-Rule-Guided Compositional Representation Learning on Knowledge Graphs.md'],\n",
       "  '[[ac-lab-Qingao University]]': ['paper-Rule-Guided Compositional Representation Learning on Knowledge Graphs.md'],\n",
       "  '[[ac-lab-State Key Laboratory of Virtual Reality Technology and Systems]]': ['paper-Rule-Guided Compositional Representation Learning on Knowledge Graphs.md'],\n",
       "  '[[ac-lab-Heidelberg University]]': ['paper-Self-supervised viewpoint learning from image collections.md'],\n",
       "  '[[ac-lab-Department of Computer Science': ['paper-Simcse Simple contrastive learning of sentence embbeddings.md'],\n",
       "  '[[ac-lab-iversity of Toronto]]': ['paper-Siamese neural networks for one-shot image recognition.md'],\n",
       "  '[[ac-lab-Bell]]': ['paper-Signature verification using a siamese time delay neural network.md'],\n",
       "  '[[ac-lab-NCR Corporation]]': ['paper-Signature verification using a siamese time delay neural network.md'],\n",
       "  '[[ac-lab-inceton University]]': ['paper-Simcse Simple contrastive learning of sentence embbeddings.md'],\n",
       "  '[[ac-lab-Tsinghua University Institute for Interdisciplinary Information Sciences]]': ['paper-Simcse Simple contrastive learning of sentence embbeddings.md'],\n",
       "  '[[ac-lab-State University of New York at Buffalo]]': ['paper-Theory of Keyblock-based image retrieval.md'],\n",
       "  '[[ac-lab-Rensselaer Polytechnic Institute]]': ['paper-Type-augmented Relation Prediction in Knowledge Graphs.md'],\n",
       "  '[[ac-lab-IBM Research]]': ['paper-Type-augmented Relation Prediction in Knowledge Graphs.md'],\n",
       "  '[[ac-lab-The Visual Defence Intelligence Network]]': ['paper-TypicFace Dynamic Margin Cosine Loss for Deep Face Recognition.md'],\n",
       "  '[[ac-lab-Amazon Rekognition]]': ['paper-Unsupervised feature learning via non-parametric instance discrimination.md'],\n",
       "  '[[ac-lab-Hebrew University of Jerusalem]]': ['paper-Why do deep convolutional networks generalize so poorly to small image transformations.md']},\n",
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       "  '[[ac-pub-None]]': ['paper-Von Mises-Fisher mixture model-based deep learning Application to face verification.md'],\n",
       "  '[[ac-pub-3D Representation and Recognition]]': ['paper-3D Object Representations for FineGrained Categorization.md'],\n",
       "  '[[ac-pub-workshop]]': ['paper-Visual categorization with bags of keypoints.md'],\n",
       "  '[[ac-pub-CVPR]]': ['paper-When Does Contrastive Visual Representation Learning Work.md'],\n",
       "  '[[ac-pub-ICIP]]': ['paper-Qamface Quadratic Additive Angular Margin Loss For Face Recognition.md'],\n",
       "  '[[ac-pub-ECCV]]': ['paper-Visual categorization with bags of keypoints.md'],\n",
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       "  '[[ac-pub-COLING]]': ['paper-Few-shot Pseudo-Labeling for Intent Detection.md'],\n",
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       "  '[[ac-pub-ICLR]]': ['paper-Understanding Neural Networks Through Deep Visualization.md'],\n",
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       "  '[[ac-pub-NeurIPS]]': ['paper-Weight Normalization A Simple Reparameterization to Accelerate Training of Deep Neural Networks.md'],\n",
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       "  '[[ac-pub-NAACL]]': ['paper-Long-tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution Networks.md'],\n",
       "  '[[ac-pub-Neurocomputing]]': ['paper-TCCT Tightly-coupled convolutional transformer on time series forecasting.md'],\n",
       "  '[[ac-pub-TIP]]': ['paper-CamStyle A Novel Data Augmentation Method for Person Re-Identification.md'],\n",
       "  '[[ac-pub-TNNLS]]': ['paper-Data Augmentation-Based Joint Learning for Heterogeneous Face Recognition.md'],\n",
       "  '[[ac-pub-Report]]': ['paper-Labeled Faces in the Wild Updates and New Reporting Procedures.md'],\n",
       "  '[[ac-pub-AAAI]]': ['paper-Weakly Supervised Video Moment Localization with Contrastive Negative Sample Mining.md'],\n",
       "  '[[ac-pub-SIGKDD]]': ['paper-On sampling strategies for neural networkbased collaborative filtering.md'],\n",
       "  '[[ac-pub-IVC]]': ['paper-Cross-resolution learning for Face Recognition.md'],\n",
       "  '[[ac-pub-BMVC]]': ['paper-Multicolumn Networks for Face Recognition.md'],\n",
       "  '[[ac-pub-HSI]]': ['paper-Deep features class activation map for thermal face detection and tracking.md'],\n",
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       "  '[[ac-pub-TPAMI]]': ['paper-Trunk-Branch Ensemble Convolutional Neural Networks for Video-Based Face Recognition.md'],\n",
       "  '[[ac-pub-IJCV]]': ['paper-Large-scale Bisample Learning on ID Versus Spot Face Recognition.md'],\n",
       "  '[[ac-pub-FG]]': ['paper-FocusFace Multi-task Contrastive Learning for Masked Face Recognition.md'],\n",
       "  '[[ac-pub-WACV]]': ['paper-QMagFace Simple and Accurate Quality-Aware Face Recognition.md'],\n",
       "  '[[ac-pub-ACM Comput Surv]]': ['paper-Generalizing from a Few Examples A Survey on Few-shot Learning.md'],\n",
       "  '[[ac-pub-Science]]': ['paper-Structured statistical models of inductive reasoning.md'],\n",
       "  '[[ac-pub-Nature]]': ['paper-Human-level control through deep reinforcement learning.md'],\n",
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       "  '[[ac-pub-SIGGRAPH]]': ['paper-Joint embeddings of shapes and images via cnn image purification.md'],\n",
       "  '[[ac-pub-EMNLP]]': ['paper-Spherical Latent Spaces for Stable Variational Autoencoders.md'],\n",
       "  '[[ac-pub-CIKM]]': ['paper-Large margin prototypical network for few-shot relation classification with fine-grained features.md'],\n",
       "  '[[ac-pub-JMLR]]': ['paper-Why do deep convolutional networks generalize so poorly to small image transformations.md'],\n",
       "  '[[ac-pub-IET]]': ['paper-Low-resolution face alignment and recognition using mixed-resolution classifiers.md'],\n",
       "  '[[ac-pub-ITech]]': ['paper-Modelling Uncertainty in Representation of Facial Features for Face Recognition.md'],\n",
       "  '[[ac-pub-ISPRS]]': ['paper-MULTI-TEMPORAL SAR IMAGE DESPECKLING BASED A CONVOLUTIONAL NEURAL NETWORK.md'],\n",
       "  '[[ac-pub-Science China Technological Sciences]]': ['paper-UAV-based integrated multispectral-LiDAR imaging system and data processing.md'],\n",
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       "  '[[ac-pub-TIFS]]': ['paper-Sparse Low-Rank Component-Based Representation for Face Recognition With Low-Quality Images.md'],\n",
       "  '[[ac-pub-TCSVT]]': ['paper-Simultaneous Hallucination and Recognition of Low-Resolution Faces Based on Singular Value Decomposition.md'],\n",
       "  '[[ac-pub-IJCAI2021]]': ['paper-Wsabie Scaling up to large vocabulary image annotation.md'],\n",
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       "  '[[ac-pub-Artif. Intell]]': ['paper-Robust Learning with Imperfect Priviledged Information.md'],\n",
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       "  '[[ac-pub-ACM Trans Inf Syst]]': ['paper-Theory of Keyblock-based image retrieval.md'],\n",
       "  '[[ac-pub- IEEE Trans Pattern Anal Mach Intell]]': ['paper-Toward open set recognition.md'],\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>path</th>\n",
       "      <th>name</th>\n",
       "      <th>dir</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1327</th>\n",
       "      <td>C:\\Users\\isidore\\OneDrive\\documents\\noteasy\\pa...</td>\n",
       "      <td>香港中文大学(深圳).md</td>\n",
       "      <td>C:\\Users\\isidore\\OneDrive\\documents\\noteasy\\pages</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1328</th>\n",
       "      <td>C:\\Users\\isidore\\OneDrive\\documents\\noteasy\\pa...</td>\n",
       "      <td>香港理工计算机.md</td>\n",
       "      <td>C:\\Users\\isidore\\OneDrive\\documents\\noteasy\\pages</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1329</th>\n",
       "      <td>C:\\Users\\isidore\\OneDrive\\documents\\noteasy\\pa...</td>\n",
       "      <td>香港科技大学.md</td>\n",
       "      <td>C:\\Users\\isidore\\OneDrive\\documents\\noteasy\\pages</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1330</th>\n",
       "      <td>C:\\Users\\isidore\\OneDrive\\documents\\noteasy\\pa...</td>\n",
       "      <td>黎曼空间.md</td>\n",
       "      <td>C:\\Users\\isidore\\OneDrive\\documents\\noteasy\\pages</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1331</th>\n",
       "      <td>C:\\Users\\isidore\\OneDrive\\documents\\noteasy\\pa...</td>\n",
       "      <td>龙骨.md</td>\n",
       "      <td>C:\\Users\\isidore\\OneDrive\\documents\\noteasy\\pages</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   path           name  \\\n",
       "1327  C:\\Users\\isidore\\OneDrive\\documents\\noteasy\\pa...  香港中文大学(深圳).md   \n",
       "1328  C:\\Users\\isidore\\OneDrive\\documents\\noteasy\\pa...     香港理工计算机.md   \n",
       "1329  C:\\Users\\isidore\\OneDrive\\documents\\noteasy\\pa...      香港科技大学.md   \n",
       "1330  C:\\Users\\isidore\\OneDrive\\documents\\noteasy\\pa...        黎曼空间.md   \n",
       "1331  C:\\Users\\isidore\\OneDrive\\documents\\noteasy\\pa...          龙骨.md   \n",
       "\n",
       "                                                    dir  \n",
       "1327  C:\\Users\\isidore\\OneDrive\\documents\\noteasy\\pages  \n",
       "1328  C:\\Users\\isidore\\OneDrive\\documents\\noteasy\\pages  \n",
       "1329  C:\\Users\\isidore\\OneDrive\\documents\\noteasy\\pages  \n",
       "1330  C:\\Users\\isidore\\OneDrive\\documents\\noteasy\\pages  \n",
       "1331  C:\\Users\\isidore\\OneDrive\\documents\\noteasy\\pages  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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